many test failures on windows 64
Hi, I am using Python.org amd64 python build on windows 7 64 bit. I am using numpy and scipy builds from here: http://www.lfd.uci.edu/~gohlke/pythonlibs/ I get many errors in scipy test (none for numpy). Particularly in scipy.sparse.linalg which I need to use (and in my code it appears spsolve is giving incorrect results). Is there a better 64 bit windows build to use?
scipy.test() Running unit tests for scipy NumPy version 1.4.1 NumPy is installed in C:\Python26\lib\site-packages\numpy SciPy version 0.8.0b1 SciPy is installed in C:\Python26\lib\site-packages\scipy Python version 2.6.5 (r265:79096, Mar 19 2010, 18:02:59) [MSC v.1500 64 bit (AMD64)] nose version 0.11.3 ........................................................................................................................ ............................E....................................F...................................................... ..............................C:\Python26\lib\site-packages\scipy\interpolate\fitpack2.py:639: 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) .....C:\Python26\lib\site-packages\scipy\interpolate\fitpack2.py:580: UserWarning: The required storage space exceeds the available storage space: nxest or nyest too small, or s too small. The weighted least-squares spline corresponds to the current set of knots. warnings.warn(message) ...........................................K..K......................................................................... ........................................................................................................................ ........................................................................................................................ EC:\Python26\lib\site-packages\numpy\lib\utils.py:140: DeprecationWarning: `write_array` is deprecated!
This function is replaced by numpy.savetxt which allows the same functionality through a different syntax. warnings.warn(depdoc, DeprecationWarning) C:\Python26\lib\site-packages\numpy\lib\utils.py:140: DeprecationWarning: `read_array` is deprecated! The functionality of read_array is in numpy.loadtxt which allows the same functionality using different syntax. warnings.warn(depdoc, DeprecationWarning) ...........................................Exception AttributeError: "'netcdf_file' object has no attribute 'mode'" in < bound method netcdf_file.close of <scipy.io.netcdf.netcdf_file object at 0x000000000C64D6D8>> ignored ............C:\Python26\lib\site-packages\numpy\lib\utils.py:140: DeprecationWarning: `npfile` is deprecated! You can achieve the same effect as using npfile using numpy.save and numpy.load. You can use memory-mapped arrays and data-types to map out a file format for direct manipulation in NumPy. warnings.warn(depdoc, DeprecationWarning) .........C:\Python26\lib\site-packages\scipy\io\wavfile.py:30: WavFileWarning: Unfamiliar format bytes warnings.warn("Unfamiliar format bytes", WavFileWarning) C:\Python26\lib\site-packages\scipy\io\wavfile.py:120: WavFileWarning: chunk not understood warnings.warn("chunk not understood", WavFileWarning) ........................................................................................................................ .......................................................................................................SSSSSS......SSSSS S......SSSS...............................................................S............................................. ........................................................................................................................ ..............................................E......................................................................... ........................................................................................................................ ....SSS.........S....................................................................................................... .............................................................F.......................................................... ........................................................................................................................ .....................................................FFF.....................................................C:\Python26 \lib\site-packages\scipy\signal\filter_design.py:247: BadCoefficients: Badly conditioned filter coefficients (numerator) : the results may be meaningless "results may be meaningless", BadCoefficients) ........................................................................................................................ ................................................................................................E....................... ..........................SSSSSSSSSSS.FE.EE.EE......K.........E.E...................................E................... ....................................................................K..................E................................ ............K..................E.................................................E...................................... ..............................................KK........................E............................................... ........................................................................................................................ ..................................................................................................................F..... ...............................................................................K.K...................................... ........................................................................................................................ ........................................................................................................................ ..........................F...F..............................................................K........K.........SSSSS... ........................................................................................................................ ........................................................................................................................ ........................................................................................................................ ........................................................................................................................ .............................S.......................................................................................... ...........................................................................................C:\Python26\lib\site-packages \scipy\stats\morestats.py:702: UserWarning: Ties preclude use of exact statistic. warnings.warn("Ties preclude use of exact statistic.") ........................................................................................................................ ........................................................................................................................ ....................................................error removing c:\users\robince\appdata\local\temp\tmpr3s_aecat_test : c:\users\robince\appdata\local\temp\tmpr3s_aecat_test: The directory is not empty .................................................................................................. ====================================================================== ERROR: Testing that kmeans2 init methods work. ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\cluster\tests\test_vq.py", line 166, in test_kmeans2_init kmeans2(data, 3, minit = 'points') File "C:\Python26\lib\site-packages\scipy\cluster\vq.py", line 671, in kmeans2 clusters = init(data, k) File "C:\Python26\lib\site-packages\scipy\cluster\vq.py", line 523, in _kpoints p = np.random.permutation(n) File "mtrand.pyx", line 4231, in mtrand.RandomState.permutation (build\scons\numpy\random\mtrand\mtrand.c:18669) File "mtrand.pyx", line 4174, in mtrand.RandomState.shuffle (build\scons\numpy\random\mtrand\mtrand.c:18261) TypeError: len() of unsized object ====================================================================== ERROR: test_basic (test_array_import.TestNumpyio) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\io\tests\test_array_import.py", line 29, in test_basic b = numpyio.fread(fid,1000000,N.Int16,N.Int) MemoryError ====================================================================== ERROR: test_decomp.test_lapack_misaligned(<function solve at 0x0000000006366438>, (array([[ 1.734e-255, 8.189e-217, 4.025e-178, 1.903e-139, 9.344e-101, ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\linalg\tests\test_decomp.py", line 1074, in check_lapack_misaligned func(*a,**kwargs) File "C:\Python26\lib\site-packages\scipy\linalg\basic.py", line 49, in solve a1, b1 = map(asarray_chkfinite,(a,b)) File "C:\Python26\lib\site-packages\numpy\lib\function_base.py", line 586, in asarray_chkfinite raise ValueError, "array must not contain infs or NaNs" ValueError: array must not contain infs or NaNs ====================================================================== ERROR: Regression test for #880: empty array for zi crashes. ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\signal\tests\test_signaltools.py", line 422, in test_empty_zi y, zf = lfilter(b, a, x, zi=zi) File "C:\Python26\lib\site-packages\scipy\signal\signaltools.py", line 610, in lfilter return sigtools._linear_filter(b, a, x, axis, zi) TypeError: array cannot be safely cast to required type ====================================================================== ERROR: test_linsolve.TestSplu.test_lu_refcount ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 122, in test_lu_refcount lu = splu(a_) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: test_linsolve.TestSplu.test_spilu_smoketest ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 60, in test_spilu_smokete st lu = spilu(self.A, drop_tol=1e-2, fill_factor=5) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 245, in spilu ilu=True, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: test_linsolve.TestSplu.test_splu_basic ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 87, in test_splu_basic lu = splu(a_) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: test_linsolve.TestSplu.test_splu_perm ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 100, in test_splu_perm lu = splu(a_) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: test_linsolve.TestSplu.test_splu_smoketest ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 53, in test_splu_smoketes t lu = splu(self.A) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: Check that QMR works with left and right preconditioners ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 161, in test_leftright_p recond L_solver = splu(L) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: test_preconditioner (test_lgmres.TestLGMRES) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_lgmres.py", line 38, in test_preconditioner pc = splu(Am.tocsc()) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: test_mu (test_base.TestBSR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type ====================================================================== ERROR: test_mu (test_base.TestCSC) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type ====================================================================== ERROR: test_mu (test_base.TestCSR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type ====================================================================== ERROR: test_mu (test_base.TestDIA) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type ====================================================================== ERROR: test_mu (test_base.TestLIL) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type ====================================================================== FAIL: test_complex (test_basic.TestLongDoubleFailure) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\fftpack\tests\test_basic.py", line 527, in test_complex np.longcomplex) AssertionError: Type <type 'numpy.complex128'> not supported but does not fail ====================================================================== FAIL: extrema 3 ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\ndimage\tests\test_ndimage.py", line 3149, in test_extrema03 self.failUnless(numpy.all(output1[2] == output4)) AssertionError ====================================================================== FAIL: test_lorentz (test_odr.TestODR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\odr\tests\test_odr.py", line 292, in test_lorentz 3.7798193600109009e+00]), File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 765, in assert_array_almost_equal header='Arrays are not almost equal') File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 609, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal (mismatch 100.0%) x: array([ 1.00000000e+03, 1.00000000e-01, 3.80000000e+00]) y: array([ 1.43067808e+03, 1.33905090e-01, 3.77981936e+00]) ====================================================================== FAIL: test_multi (test_odr.TestODR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\odr\tests\test_odr.py", line 188, in test_multi 0.5101147161764654, 0.5173902330489161]), File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 765, in assert_array_almost_equal header='Arrays are not almost equal') File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 609, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal (mismatch 100.0%) x: array([ 4. , 2. , 7. , 0.4, 0.5]) y: array([ 4.37998803, 2.43330576, 8.00288459, 0.51011472, 0.51739023]) ====================================================================== FAIL: test_pearson (test_odr.TestODR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\odr\tests\test_odr.py", line 235, in test_pearson np.array([ 5.4767400299231674, -0.4796082367610305]), File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 765, in assert_array_almost_equal header='Arrays are not almost equal') File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 609, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal (mismatch 100.0%) x: array([ 1., 1.]) y: array([ 5.47674003, -0.47960824]) ====================================================================== FAIL: test_twodiags (test_linsolve.TestLinsolve) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 39, in test_twodiags assert( norm(b - Asp*x) < 10 * cond_A * eps ) AssertionError ====================================================================== FAIL: test_kdtree.test_vectorization.test_single_query ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\spatial\tests\test_kdtree.py", line 154, in test_single_query assert isinstance(i,int) AssertionError ====================================================================== FAIL: test_data.test_boost(<Data for arccosh: acosh_data_ipp-acosh_data>,) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\special\tests\test_data.py", line 205, in _test_factory test.check(dtype=dtype) File "C:\Python26\lib\site-packages\scipy\special\tests\testutils.py", line 187, in check assert False, "\n".join(msg) AssertionError: Max |adiff|: 1.77636e-15 Max |rdiff|: 1.09352e-13 Bad results for the following points (in output 0): 1.0000014305114746 => 0.0016914556651294794 != 0.0016914556651292944 (rdiff 1.093 5249113484058e-13) 1.000007152557373 => 0.0037822080446614169 != 0.0037822080446612951 (rdiff 3.222 0418006721235e-14) 1.0000138282775879 => 0.005258943946801071 != 0.0052589439468011014 (rdiff 5.772 5773723182603e-15) 1.0000171661376953 => 0.0058593666181291238 != 0.0058593666181292027 (rdiff 1.347 0725302071254e-14) 1.0000600814819336 => 0.01096183199218881 != 0.010961831992188852 (rdiff 3.798 0296955025714e-15) 1.0001168251037598 => 0.015285472131830317 != 0.015285472131830425 (rdiff 7.036 2795851489781e-15) 1.0001487731933594 => 0.017249319093529933 != 0.017249319093529877 (rdiff 3.218 1647826365358e-15) 1.0003981590270996 => 0.028218171738655599 != 0.028218171738655373 (rdiff 7.991 8023735059643e-15) 1.000638484954834 => 0.035732814682314498 != 0.035732814682314568 (rdiff 1.941 8828227213605e-15) 1.0010714530944824 => 0.046287402472878984 != 0.046287402472878776 (rdiff 4.497 2672043800306e-15) 1.0049939155578613 => 0.099897593086028066 != 0.099897593086027803 (rdiff 2.639 4826962588157e-15) 1.024169921875 => 0.21942279004958387 != 0.21942279004958354 (rdiff 1.517 9230348510424e-15) ====================================================================== FAIL: test_data.test_boost(<Data for arctanh: atanh_data_ipp-atanh_data>,) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\special\tests\test_data.py", line 205, in _test_factory test.check(dtype=dtype) File "C:\Python26\lib\site-packages\scipy\special\tests\testutils.py", line 187, in check assert False, "\n".join(msg) AssertionError: Max |adiff|: 6.39488e-12 Max |rdiff|: 1.01982e-12 Bad results for the following points (in output 0): -0.99999284744262695 => -6.2705920974721474 != -6.2705920974657525 (rdiff 1.019 8214973073088e-12) -0.99998283386230469 => -5.832855225376532 != -5.832855225378502 (rdiff 3.377 3849320373679e-13) ---------------------------------------------------------------------- Ran 4410 tests in 29.842s FAILED (KNOWNFAIL=11, SKIP=38, errors=16, failures=9) <nose.result.TextTestResult run=4410 errors=16 failures=9>
On Mon, Jul 5, 2010 at 5:40 AM, Robin <robince@gmail.com> wrote:
Hi,
I am using Python.org amd64 python build on windows 7 64 bit.
I am using numpy and scipy builds from here: http://www.lfd.uci.edu/~gohlke/pythonlibs/
I get many errors in scipy test (none for numpy). Particularly in scipy.sparse.linalg which I need to use (and in my code it appears spsolve is giving incorrect results).
Is there a better 64 bit windows build to use?
scipy.test() Running unit tests for scipy NumPy version 1.4.1 NumPy is installed in C:\Python26\lib\site-packages\numpy SciPy version 0.8.0b1 SciPy is installed in C:\Python26\lib\site-packages\scipy Python version 2.6.5 (r265:79096, Mar 19 2010, 18:02:59) [MSC v.1500 64 bit (AMD64)] nose version 0.11.3 ........................................................................................................................ ............................E....................................F...................................................... ..............................C:\Python26\lib\site-packages\scipy\interpolate\fitpack2.py:639: 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) .....C:\Python26\lib\site-packages\scipy\interpolate\fitpack2.py:580: UserWarning: The required storage space exceeds the available storage space: nxest or nyest too small, or s too small. The weighted least-squares spline corresponds to the current set of knots. warnings.warn(message) ...........................................K..K......................................................................... ........................................................................................................................ ........................................................................................................................ EC:\Python26\lib\site-packages\numpy\lib\utils.py:140: DeprecationWarning: `write_array` is deprecated!
This function is replaced by numpy.savetxt which allows the same functionality through a different syntax.
warnings.warn(depdoc, DeprecationWarning) C:\Python26\lib\site-packages\numpy\lib\utils.py:140: DeprecationWarning: `read_array` is deprecated!
The functionality of read_array is in numpy.loadtxt which allows the same functionality using different syntax.
warnings.warn(depdoc, DeprecationWarning) ...........................................Exception AttributeError: "'netcdf_file' object has no attribute 'mode'" in < bound method netcdf_file.close of <scipy.io.netcdf.netcdf_file object at 0x000000000C64D6D8>> ignored ............C:\Python26\lib\site-packages\numpy\lib\utils.py:140: DeprecationWarning: `npfile` is deprecated!
You can achieve the same effect as using npfile using numpy.save and numpy.load.
You can use memory-mapped arrays and data-types to map out a file format for direct manipulation in NumPy.
warnings.warn(depdoc, DeprecationWarning) .........C:\Python26\lib\site-packages\scipy\io\wavfile.py:30: WavFileWarning: Unfamiliar format bytes warnings.warn("Unfamiliar format bytes", WavFileWarning) C:\Python26\lib\site-packages\scipy\io\wavfile.py:120: WavFileWarning: chunk not understood warnings.warn("chunk not understood", WavFileWarning) ........................................................................................................................ .......................................................................................................SSSSSS......SSSSS S......SSSS...............................................................S............................................. ........................................................................................................................ ..............................................E......................................................................... ........................................................................................................................ ....SSS.........S....................................................................................................... .............................................................F.......................................................... ........................................................................................................................ .....................................................FFF.....................................................C:\Python26 \lib\site-packages\scipy\signal\filter_design.py:247: BadCoefficients: Badly conditioned filter coefficients (numerator) : the results may be meaningless "results may be meaningless", BadCoefficients) ........................................................................................................................ 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...........................................................................................C:\Python26\lib\site-packages \scipy\stats\morestats.py:702: UserWarning: Ties preclude use of exact statistic. warnings.warn("Ties preclude use of exact statistic.") ........................................................................................................................ ........................................................................................................................ ....................................................error removing c:\users\robince\appdata\local\temp\tmpr3s_aecat_test : c:\users\robince\appdata\local\temp\tmpr3s_aecat_test: The directory is not empty .................................................................................................. ====================================================================== ERROR: Testing that kmeans2 init methods work. ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\cluster\tests\test_vq.py", line 166, in test_kmeans2_init kmeans2(data, 3, minit = 'points') File "C:\Python26\lib\site-packages\scipy\cluster\vq.py", line 671, in kmeans2 clusters = init(data, k) File "C:\Python26\lib\site-packages\scipy\cluster\vq.py", line 523, in _kpoints p = np.random.permutation(n) File "mtrand.pyx", line 4231, in mtrand.RandomState.permutation (build\scons\numpy\random\mtrand\mtrand.c:18669) File "mtrand.pyx", line 4174, in mtrand.RandomState.shuffle (build\scons\numpy\random\mtrand\mtrand.c:18261) TypeError: len() of unsized object
====================================================================== ERROR: test_basic (test_array_import.TestNumpyio) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\io\tests\test_array_import.py", line 29, in test_basic b = numpyio.fread(fid,1000000,N.Int16,N.Int) MemoryError
====================================================================== ERROR: test_decomp.test_lapack_misaligned(<function solve at 0x0000000006366438>, (array([[ 1.734e-255, 8.189e-217, 4.025e-178, 1.903e-139, 9.344e-101, ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\linalg\tests\test_decomp.py", line 1074, in check_lapack_misaligned func(*a,**kwargs) File "C:\Python26\lib\site-packages\scipy\linalg\basic.py", line 49, in solve a1, b1 = map(asarray_chkfinite,(a,b)) File "C:\Python26\lib\site-packages\numpy\lib\function_base.py", line 586, in asarray_chkfinite raise ValueError, "array must not contain infs or NaNs" ValueError: array must not contain infs or NaNs
====================================================================== ERROR: Regression test for #880: empty array for zi crashes. ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\signal\tests\test_signaltools.py", line 422, in test_empty_zi y, zf = lfilter(b, a, x, zi=zi) File "C:\Python26\lib\site-packages\scipy\signal\signaltools.py", line 610, in lfilter return sigtools._linear_filter(b, a, x, axis, zi) TypeError: array cannot be safely cast to required type
====================================================================== ERROR: test_linsolve.TestSplu.test_lu_refcount ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 122, in test_lu_refcount lu = splu(a_) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular
====================================================================== ERROR: test_linsolve.TestSplu.test_spilu_smoketest ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 60, in test_spilu_smokete st lu = spilu(self.A, drop_tol=1e-2, fill_factor=5) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 245, in spilu ilu=True, options=_options) RuntimeError: Factor is exactly singular
====================================================================== ERROR: test_linsolve.TestSplu.test_splu_basic ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 87, in test_splu_basic lu = splu(a_) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular
====================================================================== ERROR: test_linsolve.TestSplu.test_splu_perm ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 100, in test_splu_perm lu = splu(a_) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular
====================================================================== ERROR: test_linsolve.TestSplu.test_splu_smoketest ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 53, in test_splu_smoketes t lu = splu(self.A) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular
====================================================================== ERROR: Check that QMR works with left and right preconditioners ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 161, in test_leftright_p recond L_solver = splu(L) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular
====================================================================== ERROR: test_preconditioner (test_lgmres.TestLGMRES) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_lgmres.py", line 38, in test_preconditioner pc = splu(Am.tocsc()) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular
====================================================================== ERROR: test_mu (test_base.TestBSR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type
====================================================================== ERROR: test_mu (test_base.TestCSC) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type
====================================================================== ERROR: test_mu (test_base.TestCSR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type
====================================================================== ERROR: test_mu (test_base.TestDIA) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type
====================================================================== ERROR: test_mu (test_base.TestLIL) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type
====================================================================== FAIL: test_complex (test_basic.TestLongDoubleFailure) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\fftpack\tests\test_basic.py", line 527, in test_complex np.longcomplex) AssertionError: Type <type 'numpy.complex128'> not supported but does not fail
====================================================================== FAIL: extrema 3 ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\ndimage\tests\test_ndimage.py", line 3149, in test_extrema03 self.failUnless(numpy.all(output1[2] == output4)) AssertionError
====================================================================== FAIL: test_lorentz (test_odr.TestODR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\odr\tests\test_odr.py", line 292, in test_lorentz 3.7798193600109009e+00]), File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 765, in assert_array_almost_equal header='Arrays are not almost equal') File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 609, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal
(mismatch 100.0%) x: array([ 1.00000000e+03, 1.00000000e-01, 3.80000000e+00]) y: array([ 1.43067808e+03, 1.33905090e-01, 3.77981936e+00])
====================================================================== FAIL: test_multi (test_odr.TestODR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\odr\tests\test_odr.py", line 188, in test_multi 0.5101147161764654, 0.5173902330489161]), File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 765, in assert_array_almost_equal header='Arrays are not almost equal') File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 609, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal
(mismatch 100.0%) x: array([ 4. , 2. , 7. , 0.4, 0.5]) y: array([ 4.37998803, 2.43330576, 8.00288459, 0.51011472, 0.51739023])
====================================================================== FAIL: test_pearson (test_odr.TestODR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\odr\tests\test_odr.py", line 235, in test_pearson np.array([ 5.4767400299231674, -0.4796082367610305]), File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 765, in assert_array_almost_equal header='Arrays are not almost equal') File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 609, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal
(mismatch 100.0%) x: array([ 1., 1.]) y: array([ 5.47674003, -0.47960824])
====================================================================== FAIL: test_twodiags (test_linsolve.TestLinsolve) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 39, in test_twodiags assert( norm(b - Asp*x) < 10 * cond_A * eps ) AssertionError
====================================================================== FAIL: test_kdtree.test_vectorization.test_single_query ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\spatial\tests\test_kdtree.py", line 154, in test_single_query assert isinstance(i,int) AssertionError
====================================================================== FAIL: test_data.test_boost(<Data for arccosh: acosh_data_ipp-acosh_data>,) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\special\tests\test_data.py", line 205, in _test_factory test.check(dtype=dtype) File "C:\Python26\lib\site-packages\scipy\special\tests\testutils.py", line 187, in check assert False, "\n".join(msg) AssertionError: Max |adiff|: 1.77636e-15 Max |rdiff|: 1.09352e-13 Bad results for the following points (in output 0): 1.0000014305114746 => 0.0016914556651294794 != 0.0016914556651292944 (rdiff 1.093 5249113484058e-13) 1.000007152557373 => 0.0037822080446614169 != 0.0037822080446612951 (rdiff 3.222 0418006721235e-14) 1.0000138282775879 => 0.005258943946801071 != 0.0052589439468011014 (rdiff 5.772 5773723182603e-15) 1.0000171661376953 => 0.0058593666181291238 != 0.0058593666181292027 (rdiff 1.347 0725302071254e-14) 1.0000600814819336 => 0.01096183199218881 != 0.010961831992188852 (rdiff 3.798 0296955025714e-15) 1.0001168251037598 => 0.015285472131830317 != 0.015285472131830425 (rdiff 7.036 2795851489781e-15) 1.0001487731933594 => 0.017249319093529933 != 0.017249319093529877 (rdiff 3.218 1647826365358e-15) 1.0003981590270996 => 0.028218171738655599 != 0.028218171738655373 (rdiff 7.991 8023735059643e-15) 1.000638484954834 => 0.035732814682314498 != 0.035732814682314568 (rdiff 1.941 8828227213605e-15) 1.0010714530944824 => 0.046287402472878984 != 0.046287402472878776 (rdiff 4.497 2672043800306e-15) 1.0049939155578613 => 0.099897593086028066 != 0.099897593086027803 (rdiff 2.639 4826962588157e-15) 1.024169921875 => 0.21942279004958387 != 0.21942279004958354 (rdiff 1.517 9230348510424e-15)
====================================================================== FAIL: test_data.test_boost(<Data for arctanh: atanh_data_ipp-atanh_data>,) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\special\tests\test_data.py", line 205, in _test_factory test.check(dtype=dtype) File "C:\Python26\lib\site-packages\scipy\special\tests\testutils.py", line 187, in check assert False, "\n".join(msg) AssertionError: Max |adiff|: 6.39488e-12 Max |rdiff|: 1.01982e-12 Bad results for the following points (in output 0): -0.99999284744262695 => -6.2705920974721474 != -6.2705920974657525 (rdiff 1.019 8214973073088e-12) -0.99998283386230469 => -5.832855225376532 != -5.832855225378502 (rdiff 3.377 3849320373679e-13)
---------------------------------------------------------------------- Ran 4410 tests in 29.842s
FAILED (KNOWNFAIL=11, SKIP=38, errors=16, failures=9) <nose.result.TextTestResult run=4410 errors=16 failures=9>
SciPy-User mailing list SciPy-User@scipy.org http://mail.scipy.org/mailman/listinfo/scipy-user
Under 32-bit Python and the scipy 0.8 rc1 under Windows 7 64bit, I only get the test_boost error the directory removal error (from this test: "test_create_catalog (test_catalog.TestGetCatalog) ..."). Some of the errors could be due to Window's lack of support for 64-bit like the "test_complex (test_basic.TestLongDoubleFailure)". However, you probably would have to build your own find out those if no one else has them. Given all the issues with 64-bit windows, do you really need 64-bit numpy/scipy? Bruce
scipy.test() Running unit tests for scipy NumPy version 1.4.1 NumPy is installed in E:\Python26\lib\site-packages\numpy SciPy version 0.8.0rc1 SciPy is installed in E:\Python26\lib\site-packages\scipy Python version 2.6.3 (r263rc1:75186, Oct 2 2009, 20:40:30) [MSC v.1500 32 bit (Intel)] nose version 0.11.1 [snip] ====================================================================== FAIL: test_data.test_boost(<Data for arccosh: acosh_data_ipp-acosh_data>,)
Traceback (most recent call last): File "E:\Python26\lib\site-packages\nose-0.11.1-py2.6.egg\nose\case.py", line 183, in runTest self.test(*self.arg) File "E:\Python26\lib\site-packages\scipy\special\tests\test_data.py", line 205, in _test_factory test.check(dtype=dtype) File "E:\Python26\lib\site-packages\scipy\special\tests\testutils.py", line 223, in check assert False, "\n".join(msg) AssertionError: Max |adiff|: 1.77636e-15 Max |rdiff|: 2.44233e-14 Bad results for the following points (in output 0): 1.0000014305114746 => 0.0016914556651292853 != 0.0016914556651292944 (rdiff 5.3842961637318929e-15) 1.000007152557373 => 0.0037822080446613874 != 0.0037822080446612951 (rdiff 2.4423306175913249e-14) 1.0000138282775879 => 0.0052589439468011612 != 0.0052589439468011014 (rdiff 1.1380223962570286e-14) 1.0000600814819336 => 0.010961831992188913 != 0.010961831992188852 (rdiff 5.5387933059412495e-15) 1.0001168251037598 => 0.015285472131830449 != 0.015285472131830425 (rdiff 1.5888373256788015e-15) 1.0003981590270996 => 0.028218171738655283 != 0.028218171738655373 (rdiff 3.1967209494023856e-15) ----------------------------------------------------------------------
On Tue, Jul 6, 2010 at 3:36 AM, Bruce Southey <bsouthey@gmail.com> wrote:
On Mon, Jul 5, 2010 at 5:40 AM, Robin <robince@gmail.com> wrote:
Hi,
I am using Python.org amd64 python build on windows 7 64 bit.
I am using numpy and scipy builds from here: http://www.lfd.uci.edu/~gohlke/pythonlibs/
I get many errors in scipy test (none for numpy). Particularly in scipy.sparse.linalg which I need to use (and in my code it appears spsolve is giving incorrect results).
Is there a better 64 bit windows build to use?
Under 32-bit Python and the scipy 0.8 rc1 under Windows 7 64bit, I only get the test_boost error the directory removal error (from this test: "test_create_catalog (test_catalog.TestGetCatalog) ...").
Some of the errors could be due to Window's lack of support for 64-bit like the "test_complex (test_basic.TestLongDoubleFailure)". However, you probably would have to build your own find out those if no one else has them.
I suspect there are more errors because of indices being longs instead of ints on Windows.
Given all the issues with 64-bit windows, do you really need 64-bit numpy/scipy?
Unfortunately I do... it looks like I will now have to port a lot of Python code to Matlab. I know Windows isn't very popular in the Scipy community, and I try to avoid using it when I can, but it seems Windows 7 is a lot better than previous versions. Also >4GB RAM is now more or less standard for numerical work so I think 64 bit windows really should be supported. In my group a large factor in the decision to use windows was remote desktop and terminal services... For non-command line users there is nothing equivalent that I know of. (There is NX for linux but only 2 users is free - with a small tweak to windows 7 it is possible to have full terminal server behaviour). I wonder how enthought get around this problem with 64 bit EPD on windows? Cheers Robin
Bruce
scipy.test() Running unit tests for scipy NumPy version 1.4.1 NumPy is installed in E:\Python26\lib\site-packages\numpy SciPy version 0.8.0rc1 SciPy is installed in E:\Python26\lib\site-packages\scipy Python version 2.6.3 (r263rc1:75186, Oct 2 2009, 20:40:30) [MSC v.1500 32 bit (Intel)] nose version 0.11.1 [snip] ====================================================================== FAIL: test_data.test_boost(<Data for arccosh: acosh_data_ipp-acosh_data>,)
Traceback (most recent call last): File "E:\Python26\lib\site-packages\nose-0.11.1-py2.6.egg\nose\case.py", line 183, in runTest self.test(*self.arg) File "E:\Python26\lib\site-packages\scipy\special\tests\test_data.py", line 205, in _test_factory test.check(dtype=dtype) File "E:\Python26\lib\site-packages\scipy\special\tests\testutils.py", line 223, in check assert False, "\n".join(msg) AssertionError: Max |adiff|: 1.77636e-15 Max |rdiff|: 2.44233e-14 Bad results for the following points (in output 0): 1.0000014305114746 => 0.0016914556651292853 != 0.0016914556651292944 (rdiff 5.3842961637318929e-15) 1.000007152557373 => 0.0037822080446613874 != 0.0037822080446612951 (rdiff 2.4423306175913249e-14) 1.0000138282775879 => 0.0052589439468011612 != 0.0052589439468011014 (rdiff 1.1380223962570286e-14) 1.0000600814819336 => 0.010961831992188913 != 0.010961831992188852 (rdiff 5.5387933059412495e-15) 1.0001168251037598 => 0.015285472131830449 != 0.015285472131830425 (rdiff 1.5888373256788015e-15) 1.0003981590270996 => 0.028218171738655283 != 0.028218171738655373 (rdiff 3.1967209494023856e-15)
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On 06/07/10 23:03, Robin wrote:
On Tue, Jul 6, 2010 at 3:36 AM, Bruce Southey<bsouthey@gmail.com> wrote:
On Mon, Jul 5, 2010 at 5:40 AM, Robin<robince@gmail.com> wrote:
Hi,
I am using Python.org amd64 python build on windows 7 64 bit.
I am using numpy and scipy builds from here: http://www.lfd.uci.edu/~gohlke/pythonlibs/
I get many errors in scipy test (none for numpy). Particularly in scipy.sparse.linalg which I need to use (and in my code it appears spsolve is giving incorrect results).
Is there a better 64 bit windows build to use?
Under 32-bit Python and the scipy 0.8 rc1 under Windows 7 64bit, I only get the test_boost error the directory removal error (from this test: "test_create_catalog (test_catalog.TestGetCatalog) ...").
Some of the errors could be due to Window's lack of support for 64-bit like the "test_complex (test_basic.TestLongDoubleFailure)". However, you probably would have to build your own find out those if no one else has them.
I suspect there are more errors because of indices being longs instead of ints on Windows.
Given all the issues with 64-bit windows, do you really need 64-bit numpy/scipy?
Unfortunately I do... it looks like I will now have to port a lot of Python code to Matlab. I know Windows isn't very popular in the Scipy community, and I try to avoid using it when I can, but it seems Windows 7 is a lot better than previous versions. Also>4GB RAM is now more or less standard for numerical work so I think 64 bit windows really should be supported. In my group a large factor in the decision to use windows was remote desktop and terminal services... For non-command line users there is nothing equivalent that I know of. (There is NX for linux but only 2 users is free - with a small tweak to windows 7 it is possible to have full terminal server behaviour).
Just for the record, there's a large number of remote desktop solutions for Linux: remote X, VNC, NX and there's NeatX which is an open source NX server writtten by Google. Sorry doesn't help with your problems though.
I wonder how enthought get around this problem with 64 bit EPD on windows?
Cheers
Robin
Bruce
scipy.test() Running unit tests for scipy NumPy version 1.4.1 NumPy is installed in E:\Python26\lib\site-packages\numpy SciPy version 0.8.0rc1 SciPy is installed in E:\Python26\lib\site-packages\scipy Python version 2.6.3 (r263rc1:75186, Oct 2 2009, 20:40:30) [MSC v.1500 32 bit (Intel)] nose version 0.11.1 [snip] ====================================================================== FAIL: test_data.test_boost(<Data for arccosh: acosh_data_ipp-acosh_data>,)
Traceback (most recent call last): File "E:\Python26\lib\site-packages\nose-0.11.1-py2.6.egg\nose\case.py", line 183, in runTest self.test(*self.arg) File "E:\Python26\lib\site-packages\scipy\special\tests\test_data.py", line 205, in _test_factory test.check(dtype=dtype) File "E:\Python26\lib\site-packages\scipy\special\tests\testutils.py", line 223, in check assert False, "\n".join(msg) AssertionError: Max |adiff|: 1.77636e-15 Max |rdiff|: 2.44233e-14 Bad results for the following points (in output 0): 1.0000014305114746 => 0.0016914556651292853 != 0.0016914556651292944 (rdiff 5.3842961637318929e-15) 1.000007152557373 => 0.0037822080446613874 != 0.0037822080446612951 (rdiff 2.4423306175913249e-14) 1.0000138282775879 => 0.0052589439468011612 != 0.0052589439468011014 (rdiff 1.1380223962570286e-14) 1.0000600814819336 => 0.010961831992188913 != 0.010961831992188852 (rdiff 5.5387933059412495e-15) 1.0001168251037598 => 0.015285472131830449 != 0.015285472131830425 (rdiff 1.5888373256788015e-15) 1.0003981590270996 => 0.028218171738655283 != 0.028218171738655373 (rdiff 3.1967209494023856e-15)
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_______________________________________________ SciPy-User mailing list SciPy-User@scipy.org http://mail.scipy.org/mailman/listinfo/scipy-user
2010/7/6 Jochen Schröder <cycomanic@gmail.com>:
Just for the record, there's a large number of remote desktop solutions for Linux: remote X, VNC, NX and there's NeatX which is an open source NX server writtten by Google. Sorry doesn't help with your problems though.
Thanks... I find X and VNC are not really comparable to RDP in terms of usability (eg performance, connecting from different resolutions, keeping sessions, suitability for windows users etc.) NX is great but quite expensive. I didn't know about NeatX so I will have a look. But it doesn't have a release yet so I imagine its a little experimental. I stil think that for sharing a well equipped workstation among a small laboratory group (with no command line experience) win 7 and remote desktop is the best solution. Cheers Robin
Robin wrote:
NX is great but quite expensive.
yes, though it looks cheaper than MATLAB. But if you've got people that want to use Windows, you've got people that want to use Windows. -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker@noaa.gov
On Tue, Jul 6, 2010 at 11:17 AM, Christopher Barker <Chris.Barker@noaa.gov>wrote:
Robin wrote:
NX is great but quite expensive.
yes, though it looks cheaper than MATLAB. But if you've got people that want to use Windows, you've got people that want to use Windows.
Let's get this thread back to the errors. The problems seem specific to the python.org amd64 python, is that correct? Chuck
Tue, 06 Jul 2010 11:53:22 -0600, Charles R Harris wrote: [clip]
Let's get this thread back to the errors. The problems seem specific to the python.org amd64 python, is that correct?
The SuperLU failures puzzle me. It should be "straightforward" C code, and I don't understand what can go wrong there. The "Factor is exactly singular" error indicates essentially means that SuperLU thinks it detects a zero pivot or something, so something seems to fail at a fairly low level. This seems quite difficult to debug without a Win-64 at hand. Another thing is that Gohlke's binaries are also built against MKL, and SuperLU does call BLAS routines. I wonder if something can break because of that... @Robin: Also, for the cases where a wrong result is produced with no error: is it easy to write a small test program demonstrating this? If yes, could you write one? -- Pauli Virtanen
On 7/6/2010 11:43 AM, Pauli Virtanen wrote:
Tue, 06 Jul 2010 11:53:22 -0600, Charles R Harris wrote: [clip]
Let's get this thread back to the errors. The problems seem specific to the python.org amd64 python, is that correct?
No, the unofficial scipy-0.8.0rc1.win32-py2.6 build fails with (mostly) the same errors. Most of the reported errors are apparently specific to my build environment: msvc9, ifort 11.1, mkl 10.2, numscons 0.12.0dev.
The SuperLU failures puzzle me. It should be "straightforward" C code, and I don't understand what can go wrong there. The "Factor is exactly singular" error indicates essentially means that SuperLU thinks it detects a zero pivot or something, so something seems to fail at a fairly low level.
This seems quite difficult to debug without a Win-64 at hand.
Another thing is that Gohlke's binaries are also built against MKL, and SuperLU does call BLAS routines. I wonder if something can break because of that...
@Robin: Also, for the cases where a wrong result is produced with no error: is it easy to write a small test program demonstrating this? If yes, could you write one?
-- Christoph
On Tue, Jul 6, 2010 at 7:43 PM, Pauli Virtanen <pav@iki.fi> wrote:
Tue, 06 Jul 2010 11:53:22 -0600, Charles R Harris wrote: [clip]
Let's get this thread back to the errors. The problems seem specific to the python.org amd64 python, is that correct?
The SuperLU failures puzzle me. It should be "straightforward" C code, and I don't understand what can go wrong there. The "Factor is exactly singular" error indicates essentially means that SuperLU thinks it detects a zero pivot or something, so something seems to fail at a fairly low level.
This seems quite difficult to debug without a Win-64 at hand.
Another thing is that Gohlke's binaries are also built against MKL, and SuperLU does call BLAS routines. I wonder if something can break because of that...
@Robin: Also, for the cases where a wrong result is produced with no error: is it easy to write a small test program demonstrating this? If yes, could you write one?
Yes, below is a simple example. On my mac it works: In [10]: run -i sptest.py [-0.34841705 -0.23272338 0.27248558] [-0.34841705 -0.23272338 0.27248558] On the 64bit windows installation: In [7]: run -i spsolve_test.py [ 0.08507826 1.04401349 -1.56609783] [-0.52208434 -1.48101957 -1.56609783] In [8]: run -i spsolve_test.py [ 0.71923676 -0.12209489 -0.16069061] [-0.2827855 0.55854616 -0.16069061] import numpy as np import scipy as sp import scipy.sparse.linalg Adense = np.matrix([[ 0., 1., 1.], [ 1., 0., 1.], [ 0., 0., 1.]]) As = sp.sparse.csc_matrix(Adense) x = np.random.randn(3) b = As.matvec(x) print x print sp.sparse.linalg.spsolve(As, b)
On Tue, Jul 6, 2010 at 1:20 PM, Robin <robince@gmail.com> wrote:
On Tue, Jul 6, 2010 at 7:43 PM, Pauli Virtanen <pav@iki.fi> wrote:
Tue, 06 Jul 2010 11:53:22 -0600, Charles R Harris wrote: [clip]
Let's get this thread back to the errors. The problems seem specific to the python.org amd64 python, is that correct?
The SuperLU failures puzzle me. It should be "straightforward" C code, and I don't understand what can go wrong there. The "Factor is exactly singular" error indicates essentially means that SuperLU thinks it detects a zero pivot or something, so something seems to fail at a fairly low level.
This seems quite difficult to debug without a Win-64 at hand.
Another thing is that Gohlke's binaries are also built against MKL, and SuperLU does call BLAS routines. I wonder if something can break because of that...
@Robin: Also, for the cases where a wrong result is produced with no error: is it easy to write a small test program demonstrating this? If yes, could you write one?
Yes, below is a simple example. On my mac it works: In [10]: run -i sptest.py [-0.34841705 -0.23272338 0.27248558] [-0.34841705 -0.23272338 0.27248558]
On the 64bit windows installation: In [7]: run -i spsolve_test.py [ 0.08507826 1.04401349 -1.56609783] [-0.52208434 -1.48101957 -1.56609783] In [8]: run -i spsolve_test.py [ 0.71923676 -0.12209489 -0.16069061] [-0.2827855 0.55854616 -0.16069061]
import numpy as np import scipy as sp import scipy.sparse.linalg
Adense = np.matrix([[ 0., 1., 1.], [ 1., 0., 1.], [ 0., 0., 1.]]) As = sp.sparse.csc_matrix(Adense) x = np.random.randn(3) b = As.matvec(x)
print x print sp.sparse.linalg.spsolve(As, b)
Is x the same on both machines? Chuck
On Tue, Jul 6, 2010 at 8:26 PM, Charles R Harris <charlesr.harris@gmail.com> wrote:
Is x the same on both machines?
No I was randomly generating each time... With the same x on both machines: mac In [23]: run -i sptest.py [-0.52196943 0.04636895 -0.39616894] [-0.52196943 0.04636895 -0.39616894] Windows 64: In [23]: run -i spsolve_test.py [-0.52196943 0.04636895 -0.39616894] [-0.34979999 -0.91813837 -0.39616894]
On 7/6/2010 11:43 AM, Pauli Virtanen wrote:
Tue, 06 Jul 2010 11:53:22 -0600, Charles R Harris wrote: [clip]
Let's get this thread back to the errors. The problems seem specific to the python.org amd64 python, is that correct?
The SuperLU failures puzzle me. It should be "straightforward" C code, and I don't understand what can go wrong there. The "Factor is exactly singular" error indicates essentially means that SuperLU thinks it detects a zero pivot or something, so something seems to fail at a fairly low level.
This seems quite difficult to debug without a Win-64 at hand.
Another thing is that Gohlke's binaries are also built against MKL, and SuperLU does call BLAS routines. I wonder if something can break because of that...
Apparently it can. When I link SuperLU against CBLAS (from the SuperLU_4.0 distribution) instead of MKL 10.2 the test errors "RuntimeError: Factor is exactly singular" disappear. Now I get many more "TypeError: array cannot be safely cast to required type" failures, but that's a different problem. -- Christoph
On Tue, Jul 6, 2010 at 6:53 PM, Charles R Harris
Let's get this thread back to the errors. The problems seem specific to the python.org amd64 python, is that correct?
Yes, that is the build I am using. The mtrand problems come from shape of arrays being longs instead of ints (since ints are 32 bit). I'm not sure if this could be having similar problems elsewhere. As Pauli noted the numpy build is with MKL - but I chose that one because it is required by the scipy amd64 build on that page... I'm not aware of any other scipy win64 builds. Cheers Robin
On Tue, Jul 6, 2010 at 9:03 PM, Robin <robince@gmail.com> wrote:
On Tue, Jul 6, 2010 at 3:36 AM, Bruce Southey <bsouthey@gmail.com> wrote:
On Mon, Jul 5, 2010 at 5:40 AM, Robin <robince@gmail.com> wrote:
Hi,
I am using Python.org amd64 python build on windows 7 64 bit.
I am using numpy and scipy builds from here: http://www.lfd.uci.edu/~gohlke/pythonlibs/<http://www.lfd.uci.edu/%7Egohlke/pythonlibs/>
I get many errors in scipy test (none for numpy). Particularly in scipy.sparse.linalg which I need to use (and in my code it appears spsolve is giving incorrect results).
Is there a better 64 bit windows build to use?
Under 32-bit Python and the scipy 0.8 rc1 under Windows 7 64bit, I only get the test_boost error the directory removal error (from this test: "test_create_catalog (test_catalog.TestGetCatalog) ...").
Some of the errors could be due to Window's lack of support for 64-bit like the "test_complex (test_basic.TestLongDoubleFailure)". However, you probably would have to build your own find out those if no one else has them.
I suspect there are more errors because of indices being longs instead of ints on Windows.
Given all the issues with 64-bit windows, do you really need 64-bit numpy/scipy?
Unfortunately I do... it looks like I will now have to port a lot of Python code to Matlab.
First about the test output: in 0.8.0rc1 all printed warnings, the lapack_misaligned and the npyio errors are gone. The boost errors will be gone in the final release as well. So you have about 20 errors/failures left, mostly located in the sparse and odr modules. Unless you're a heavy user of those, no need to move to matlab. You could also decide to look into the errors instead of rewriting your code.
I know Windows isn't very popular in the Scipy community, and I try to avoid using it when I can, but it seems Windows 7 is a lot better than previous versions. Also >4GB RAM is now more or less standard for numerical work so I think 64 bit windows really should be supported. In my group a large factor in the decision to use windows was remote desktop and terminal services... For non-command line users there is nothing equivalent that I know of. (There is NX for linux but only 2 users is free - with a small tweak to windows 7 it is possible to have full terminal server behaviour).
I wonder how enthought get around this problem with 64 bit EPD on windows?
So why not use EPD? Still many times cheaper than Matlab.... Cheers, Ralf
On Tue, Jul 6, 2010 at 2:46 PM, Ralf Gommers <ralf.gommers@googlemail.com> wrote:
First about the test output: in 0.8.0rc1 all printed warnings, the lapack_misaligned and the npyio errors are gone. The boost errors will be gone in the final release as well. So you have about 20 errors/failures left, mostly located in the sparse and odr modules. Unless you're a heavy user of those, no need to move to matlab. You could also decide to look into the errors instead of rewriting your code.
Thanks... I depend on the sparse module quite heavily which is why it's a problem. (my code that uses spsolve is giving incorrect results, although no errors). It's probably not as bad as I made out though - I'm sure I can do a fair bit with 32 bit Python, it's just more difficult to make it accessible to my colleagues (will have to install 32 bit MATLAB and they will have to pay attention to which one they are using).
I know Windows isn't very popular in the Scipy community, and I try to avoid using it when I can, but it seems Windows 7 is a lot better than previous versions. Also >4GB RAM is now more or less standard for numerical work so I think 64 bit windows really should be supported. In my group a large factor in the decision to use windows was remote desktop and terminal services... For non-command line users there is nothing equivalent that I know of. (There is NX for linux but only 2 users is free - with a small tweak to windows 7 it is possible to have full terminal server behaviour).
I wonder how enthought get around this problem with 64 bit EPD on windows?
So why not use EPD? Still many times cheaper than Matlab....
I would suggest that if I had any influence at all on purchasing decisions, but as a PhD student I don't. Also MATLAB license is paid for by central IT, whereas any extra software would have to come out of group grants. If I wasn't the only person using it there might be a case, but unless I can get it working on Windows I'll continue to be the only person using it! (bit of a chicken and egg). Cheers Robin
On 07/06/2010 08:03 AM, Robin wrote:
On Tue, Jul 6, 2010 at 3:36 AM, Bruce Southey<bsouthey@gmail.com> wrote:
On Mon, Jul 5, 2010 at 5:40 AM, Robin<robince@gmail.com> wrote:
Hi,
I am using Python.org amd64 python build on windows 7 64 bit.
I am using numpy and scipy builds from here: http://www.lfd.uci.edu/~gohlke/pythonlibs/
I get many errors in scipy test (none for numpy). Particularly in scipy.sparse.linalg which I need to use (and in my code it appears spsolve is giving incorrect results).
Is there a better 64 bit windows build to use?
Under 32-bit Python and the scipy 0.8 rc1 under Windows 7 64bit, I only get the test_boost error the directory removal error (from this test: "test_create_catalog (test_catalog.TestGetCatalog) ...").
Some of the errors could be due to Window's lack of support for 64-bit like the "test_complex (test_basic.TestLongDoubleFailure)". However, you probably would have to build your own find out those if no one else has them.
I suspect there are more errors because of indices being longs instead of ints on Windows.
Given all the issues with 64-bit windows, do you really need 64-bit numpy/scipy?
Unfortunately I do... it looks like I will now have to port a lot of Python code to Matlab. I know Windows isn't very popular in the Scipy community, and I try to avoid using it when I can, but it seems Windows 7 is a lot better than previous versions. Windows 7 is a big improvement over Vista but both suffer the
It would be great to track some of these down. Basically scipy has not had the attention that numpy has in this matter eventhough David Cournapeau done really incredible work in getting numpy/scipy to work under 64-bit Windows. transisition from 32-bit to x64 64-bit architecture (similar to Linux when these x64 cpu's came out). Sure most people do not develop with Windows but do not equate that with a lack of interest. The problem is that Windows and how the Windows binaries are build just makes it very extremely hard to develop for.
Also>4GB RAM is now more or less standard for numerical work so I think 64 bit windows really should be supported. Yes, there are many people who want it but the tools are too complex to use by casual people.
In my group a large factor in the decision to use windows was remote desktop and terminal services... For non-command line users there is nothing equivalent that I know of. (There is NX for linux but only 2 users is free - with a small tweak to windows 7 it is possible to have full terminal server behaviour).
You tried FreeNx? http://freenx.berlios.de/ While this is really old (and has some big issues including not being maintained) but I occasionally use xrdp as you can connect to Linux with Windows remote desktop. "RDP Server - An open source RDP server and X server capable of accepting connections from rdesktop and ms terminal server clients." http://xrdp.sourceforge.net/
I wonder how enthought get around this problem with 64 bit EPD on windows?
Cheers
Robin
Can't comment on those. Bruce
Bruce
scipy.test()
Running unit tests for scipy NumPy version 1.4.1 NumPy is installed in E:\Python26\lib\site-packages\numpy SciPy version 0.8.0rc1 SciPy is installed in E:\Python26\lib\site-packages\scipy Python version 2.6.3 (r263rc1:75186, Oct 2 2009, 20:40:30) [MSC v.1500 32 bit (Intel)] nose version 0.11.1 [snip] ====================================================================== FAIL: test_data.test_boost(<Data for arccosh: acosh_data_ipp-acosh_data>,)
Traceback (most recent call last): File "E:\Python26\lib\site-packages\nose-0.11.1-py2.6.egg\nose\case.py", line 183, in runTest self.test(*self.arg) File "E:\Python26\lib\site-packages\scipy\special\tests\test_data.py", line 205, in _test_factory test.check(dtype=dtype) File "E:\Python26\lib\site-packages\scipy\special\tests\testutils.py", line 223, in check assert False, "\n".join(msg) AssertionError: Max |adiff|: 1.77636e-15 Max |rdiff|: 2.44233e-14 Bad results for the following points (in output 0): 1.0000014305114746 => 0.0016914556651292853 != 0.0016914556651292944 (rdiff 5.3842961637318929e-15) 1.000007152557373 => 0.0037822080446613874 != 0.0037822080446612951 (rdiff 2.4423306175913249e-14) 1.0000138282775879 => 0.0052589439468011612 != 0.0052589439468011014 (rdiff 1.1380223962570286e-14) 1.0000600814819336 => 0.010961831992188913 != 0.010961831992188852 (rdiff 5.5387933059412495e-15) 1.0001168251037598 => 0.015285472131830449 != 0.015285472131830425 (rdiff 1.5888373256788015e-15) 1.0003981590270996 => 0.028218171738655283 != 0.028218171738655373 (rdiff 3.1967209494023856e-15)
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On Tue, Jul 6, 2010 at 3:04 PM, Bruce Southey <bsouthey@gmail.com> wrote:
Windows 7 is a big improvement over Vista but both suffer the transisition from 32-bit to x64 64-bit architecture (similar to Linux when these x64 cpu's came out). Sure most people do not develop with Windows but do not equate that with a lack of interest. The problem is that Windows and how the Windows binaries are build just makes it very extremely hard to develop for.
Yes, I was really surprised at this. I don't know very much about the workings of Python, but presumably theres a reason the Python people couldn't have made ints on win64 proper 64 bit ints using whatever type microsoft requires instead of just sticking with 32bit C longs. I tried not to have a gripey negative tone in the original email but perhaps I failed. It is always frustrating when you spend a lot of time on something (I spent quite a long time getting MATLAB-Python integration working on 64 bit windows... of course I should have checked numpy+scipy first!). Any way I really appreciate all the work thats gone into making numpy and scipy available... I just wanted to make the point that with windows 7 64 bit windows isn't such a joke and there are people who would use a win64 scipy stack.
You tried FreeNx? http://freenx.berlios.de/
When I tried it it was very hard to get working a bit tempramental... it was a while ago though. Also I'm the only linux user in the lab and I'm leaving soon so windows really was the only option. Cheers Robin
On Tue, Jul 6, 2010 at 4:13 PM, Robin <robince@gmail.com> wrote:
On Tue, Jul 6, 2010 at 3:04 PM, Bruce Southey <bsouthey@gmail.com> wrote:
Windows 7 is a big improvement over Vista but both suffer the transisition from 32-bit to x64 64-bit architecture (similar to Linux when these x64 cpu's came out). Sure most people do not develop with Windows but do not equate that with a lack of interest. The problem is that Windows and how the Windows binaries are build just makes it very extremely hard to develop for.
Yes, I was really surprised at this. I don't know very much about the workings of Python, but presumably theres a reason the Python people couldn't have made ints on win64 proper 64 bit ints using whatever type microsoft requires instead of just sticking with 32bit C longs.
I am not sure why you think that's the problem to the issues you are describing. Numpy does use a type which is 64 bits for indexing on windows as everywhere else, and that's not the cause of the issues you have described so far. The random shuffle limitation for example is not windows specific, for example. Concerning sparse matrices, the index is currently limited to 32 bits: you can change this by hand if you need 64 bits indexing (in sparsetools.i, add DECLARE_INDEX_TYPE(npy_intp)). David
participants (9)
-
Bruce Southey -
Charles R Harris -
Christoph Gohlke -
Christopher Barker -
David Cournapeau -
Jochen Schröder -
Pauli Virtanen -
Ralf Gommers -
Robin