Re: [Numpy-discussion] Numpy - MKL - build error
Yes, they all are present in that directory. Also, I tried with root as login. -r-xr-xr-x 1 root root 26342559 Aug 9 22:19 libmkl_avx.so -r--r--r-- 1 root root 1190224 Aug 9 22:26 libmkl_blacs_ilp64.a -r--r--r-- 1 root root 1191496 Aug 9 22:25 libmkl_blacs_intelmpi_ilp64.a -r-xr-xr-x 1 root root 497597 Aug 9 22:25 libmkl_blacs_intelmpi_ilp64.so -r--r--r-- 1 root root 676206 Aug 9 22:21 libmkl_blacs_intelmpi_lp64.a -r-xr-xr-x 1 root root 267010 Aug 9 22:21 libmkl_blacs_intelmpi_lp64.so -r--r--r-- 1 root root 674926 Aug 9 22:22 libmkl_blacs_lp64.a -r--r--r-- 1 root root 1218290 Aug 9 22:28 libmkl_blacs_openmpi_ilp64.a -r--r--r-- 1 root root 703042 Aug 9 22:23 libmkl_blacs_openmpi_lp64.a -r--r--r-- 1 root root 1191152 Aug 9 22:29 libmkl_blacs_sgimpt_ilp64.a -r--r--r-- 1 root root 675854 Aug 9 22:23 libmkl_blacs_sgimpt_lp64.a -r--r--r-- 1 root root 425802 Aug 9 20:44 libmkl_blas95_ilp64.a -r--r--r-- 1 root root 421410 Aug 9 20:44 libmkl_blas95_lp64.a -r--r--r-- 1 root root 144354 Aug 9 22:29 libmkl_cdft_core.a -r-xr-xr-x 1 root root 115588 Aug 9 22:29 libmkl_cdft_core.so -r--r--r-- 1 root root 231886824 Aug 9 22:07 libmkl_core.a -r-xr-xr-x 1 root root 16730033 Aug 9 22:18 libmkl_core.so -r-xr-xr-x 1 root root 21474555 Aug 9 22:18 libmkl_def.so -r--r--r-- 1 root root 14974574 Aug 9 22:06 libmkl_gf_ilp64.a -r-xr-xr-x 1 root root 7008828 Aug 9 22:48 libmkl_gf_ilp64.so -r--r--r-- 1 root root 15140998 Aug 9 22:06 libmkl_gf_lp64.a -r-xr-xr-x 1 root root 7055304 Aug 9 22:48 libmkl_gf_lp64.so -r--r--r-- 1 root root 16435120 Aug 9 22:07 libmkl_gnu_thread.a -r-xr-xr-x 1 root root 9816940 Aug 9 22:49 libmkl_gnu_thread.so -r--r--r-- 1 root root 14968130 Aug 9 22:06 libmkl_intel_ilp64.a -r-xr-xr-x 1 root root 7008368 Aug 9 22:48 libmkl_intel_ilp64.so -r--r--r-- 1 root root 15134406 Aug 9 22:06 libmkl_intel_lp64.a -r-xr-xr-x 1 root root 7053588 Aug 9 22:48 libmkl_intel_lp64.so -r--r--r-- 1 root root 2472940 Aug 9 22:07 libmkl_intel_sp2dp.a -r-xr-xr-x 1 root root 1191479 Aug 9 22:20 libmkl_intel_sp2dp.so -r--r--r-- 1 root root 27642508 Aug 9 22:07 libmkl_intel_thread.a -r-xr-xr-x 1 root root 17516608 Aug 9 22:49 libmkl_intel_thread.so -r--r--r-- 1 root root 5350948 Aug 9 20:44 libmkl_lapack95_ilp64.a -r--r--r-- 1 root root 5413476 Aug 9 20:44 libmkl_lapack95_lp64.a -r-xr-xr-x 1 root root 29543829 Aug 9 22:19 libmkl_mc3.so -r-xr-xr-x 1 root root 25428037 Aug 9 22:19 libmkl_mc.so -r-xr-xr-x 1 root root 22888659 Aug 9 22:18 libmkl_p4n.so -r--r--r-- 1 root root 19232716 Aug 9 22:07 libmkl_pgi_thread.a -r-xr-xr-x 1 root root 12243062 Aug 9 22:49 libmkl_pgi_thread.so -r-xr-xr-x 1 root root 4984870 Aug 9 22:49 libmkl_rt.so -r--r--r-- 1 root root 10367758 Aug 9 22:49 libmkl_scalapack_ilp64.a -r-xr-xr-x 1 root root 6574928 Aug 9 22:50 libmkl_scalapack_ilp64.so -r--r--r-- 1 root root 10292432 Aug 9 22:49 libmkl_scalapack_lp64.a -r-xr-xr-x 1 root root 6452627 Aug 9 22:50 libmkl_scalapack_lp64.so -r--r--r-- 1 root root 9958444 Aug 9 22:07 libmkl_sequential.a -r-xr-xr-x 1 root root 5926347 Aug 9 22:48 libmkl_sequential.so -r--r--r-- 1 root root 1048 Aug 9 16:50 libmkl_solver_ilp64.a -r--r--r-- 1 root root 1048 Aug 9 16:50 libmkl_solver_ilp64_sequential.a -r--r--r-- 1 root root 1048 Aug 9 16:50 libmkl_solver_lp64.a -r--r--r-- 1 root root 1048 Aug 9 16:50 libmkl_solver_lp64_sequential.a -r-xr-xr-x 1 root root 6711968 Aug 9 22:48 libmkl_vml_avx.so -r-xr-xr-x 1 root root 2795928 Aug 9 22:47 libmkl_vml_def.so -r-xr-xr-x 1 root root 5476786 Aug 9 22:48 libmkl_vml_mc2.so -r-xr-xr-x 1 root root 5778052 Aug 9 22:48 libmkl_vml_mc3.so -r-xr-xr-x 1 root root 5382511 Aug 9 22:48 libmkl_vml_mc.so -r-xr-xr-x 1 root root 4235841 Aug 9 22:48 libmkl_vml_p4n.so drwxr-xr-x 3 root root 4096 Aug 18 11:43 locale Y you can reach the person managing the list at numpy-discussion-ow...@scipy.org Message: 1 Date: Tue, 13 Sep 2011 09:58:27 -0400 From: Olivier Delalleau sh...@keba.be Subject: Re: [Numpy-discussion] Numpy - MKL - build error To: Discussion of Numerical Python numpy-discussion@scipy.org Message-ID: cafxk4bpdn7qcwmze2g565gsuontau7lmh4fgsth_zy_r+nl...@mail.gmail.com Content-Type: text/plain; charset=iso-8859-1 Sorry if it sounds like a stupid question, but are the files listed in the error message present in that directory? If yes, maybe try running the command with sudo, just in case it would be some weird permission issue. -=- Olivier 2011/9/13 Igor Ying igor.y...@yahoo.com Hi, I am very new to Numpy and trying to build Numpy 1.6.1 with Intel MKL 10.3.6 and getting the following errors. $python setup.py config --compiler=intel build_clib --compiler=intel build_ext --compiler=intel install Running from numpy source directory.F2PY Version 2 blas_opt_info: blas_mkl_info: libraries mkl_def,mkl_intel_lp64,mkl_intel_thread,mkl_core,iomp5md not
Re: [Numpy-discussion] Numpy - MKL - build error
It seems you are missing libiomp5.so, which is sound if you re using the whole Composer package: the needed libs are split in two different locations, and unfortunately, Numpy cannot cope with this last time I checked (I think it was one of the reasons David Cournapeau created numscons and bento). Matthieu 2011/9/14 Igor Ying igor.y...@yahoo.com Yes, they all are present in that directory. Also, I tried with root as login. -r-xr-xr-x 1 root root 26342559 Aug 9 22:19 libmkl_avx.so -r--r--r-- 1 root root 1190224 Aug 9 22:26 libmkl_blacs_ilp64.a -r--r--r-- 1 root root 1191496 Aug 9 22:25 libmkl_blacs_intelmpi_ilp64.a -r-xr-xr-x 1 root root497597 Aug 9 22:25 libmkl_blacs_intelmpi_ilp64.so -r--r--r-- 1 root root676206 Aug 9 22:21 libmkl_blacs_intelmpi_lp64.a -r-xr-xr-x 1 root root267010 Aug 9 22:21 libmkl_blacs_intelmpi_lp64.so -r--r--r-- 1 root root674926 Aug 9 22:22 libmkl_blacs_lp64.a -r--r--r-- 1 root root 1218290 Aug 9 22:28 libmkl_blacs_openmpi_ilp64.a -r--r--r-- 1 root root703042 Aug 9 22:23 libmkl_blacs_openmpi_lp64.a -r--r--r-- 1 root root 1191152 Aug 9 22:29 libmkl_blacs_sgimpt_ilp64.a -r--r--r-- 1 root root675854 Aug 9 22:23 libmkl_blacs_sgimpt_lp64.a -r--r--r-- 1 root root425802 Aug 9 20:44 libmkl_blas95_ilp64.a -r--r--r-- 1 root root421410 Aug 9 20:44 libmkl_blas95_lp64.a -r--r--r-- 1 root root144354 Aug 9 22:29 libmkl_cdft_core.a -r-xr-xr-x 1 root root115588 Aug 9 22:29 libmkl_cdft_core.so -r--r--r-- 1 root root 231886824 Aug 9 22:07 libmkl_core.a -r-xr-xr-x 1 root root 16730033 Aug 9 22:18 libmkl_core.so -r-xr-xr-x 1 root root 21474555 Aug 9 22:18 libmkl_def.so -r--r--r-- 1 root root 14974574 Aug 9 22:06 libmkl_gf_ilp64.a -r-xr-xr-x 1 root root 7008828 Aug 9 22:48 libmkl_gf_ilp64.so -r--r--r-- 1 root root 15140998 Aug 9 22:06 libmkl_gf_lp64.a -r-xr-xr-x 1 root root 7055304 Aug 9 22:48 libmkl_gf_lp64.so -r--r--r-- 1 root root 16435120 Aug 9 22:07 libmkl_gnu_thread.a -r-xr-xr-x 1 root root 9816940 Aug 9 22:49 libmkl_gnu_thread.so -r--r--r-- 1 root root 14968130 Aug 9 22:06 libmkl_intel_ilp64.a -r-xr-xr-x 1 root root 7008368 Aug 9 22:48 libmkl_intel_ilp64.so -r--r--r-- 1 root root 15134406 Aug 9 22:06 libmkl_intel_lp64.a -r-xr-xr-x 1 root root 7053588 Aug 9 22:48 libmkl_intel_lp64.so -r--r--r-- 1 root root 2472940 Aug 9 22:07 libmkl_intel_sp2dp.a -r-xr-xr-x 1 root root 1191479 Aug 9 22:20 libmkl_intel_sp2dp.so -r--r--r-- 1 root root 27642508 Aug 9 22:07 libmkl_intel_thread.a -r-xr-xr-x 1 root root 17516608 Aug 9 22:49 libmkl_intel_thread.so -r--r--r-- 1 root root 5350948 Aug 9 20:44 libmkl_lapack95_ilp64.a -r--r--r-- 1 root root 5413476 Aug 9 20:44 libmkl_lapack95_lp64.a -r-xr-xr-x 1 root root 29543829 Aug 9 22:19 libmkl_mc3.so -r-xr-xr-x 1 root root 25428037 Aug 9 22:19 libmkl_mc.so -r-xr-xr-x 1 root root 22888659 Aug 9 22:18 libmkl_p4n.so -r--r--r-- 1 root root 19232716 Aug 9 22:07 libmkl_pgi_thread.a -r-xr-xr-x 1 root root 12243062 Aug 9 22:49 libmkl_pgi_thread.so -r-xr-xr-x 1 root root 4984870 Aug 9 22:49 libmkl_rt.so -r--r--r-- 1 root root 10367758 Aug 9 22:49 libmkl_scalapack_ilp64.a -r-xr-xr-x 1 root root 6574928 Aug 9 22:50 libmkl_scalapack_ilp64.so -r--r--r-- 1 root root 10292432 Aug 9 22:49 libmkl_scalapack_lp64.a -r-xr-xr-x 1 root root 6452627 Aug 9 22:50 libmkl_scalapack_lp64.so -r--r--r-- 1 root root 9958444 Aug 9 22:07 libmkl_sequential.a -r-xr-xr-x 1 root root 5926347 Aug 9 22:48 libmkl_sequential.so -r--r--r-- 1 root root 1048 Aug 9 16:50 libmkl_solver_ilp64.a -r--r--r-- 1 root root 1048 Aug 9 16:50 libmkl_solver_ilp64_sequential.a -r--r--r-- 1 root root 1048 Aug 9 16:50 libmkl_solver_lp64.a -r--r--r-- 1 root root 1048 Aug 9 16:50 libmkl_solver_lp64_sequential.a -r-xr-xr-x 1 root root 6711968 Aug 9 22:48 libmkl_vml_avx.so -r-xr-xr-x 1 root root 2795928 Aug 9 22:47 libmkl_vml_def.so -r-xr-xr-x 1 root root 5476786 Aug 9 22:48 libmkl_vml_mc2.so -r-xr-xr-x 1 root root 5778052 Aug 9 22:48 libmkl_vml_mc3.so -r-xr-xr-x 1 root root 5382511 Aug 9 22:48 libmkl_vml_mc.so -r-xr-xr-x 1 root root 4235841 Aug 9 22:48 libmkl_vml_p4n.so drwxr-xr-x 3 root root 4096 Aug 18 11:43 locale Y you can reach the person managing the list at numpy-discussion-ow...@scipy.org Message: 1 Date: Tue, 13 Sep 2011 09:58:27 -0400 From: Olivier Delalleau sh...@keba.be Subject: Re: [Numpy-discussion] Numpy - MKL - build error To: Discussion of Numerical Python numpy-discussion@scipy.org Message-ID: cafxk4bpdn7qcwmze2g565gsuontau7lmh4fgsth_zy_r+nl...@mail.gmail.com Content-Type: text/plain; charset=iso-8859-1 Sorry if it sounds like a stupid question, but are the files listed in the error message present in that directory? If yes, maybe try running the command with sudo, just in case it would be some weird permission issue. -=- Olivier
Re: [Numpy-discussion] Numpy - MKL - build error
My bad, iomp5md is in compiler/lib dir, I copied it to the mkl dir and it worked. From: Igor Ying igor.y...@yahoo.com To: numpy-discussion@scipy.org numpy-discussion@scipy.org Sent: Wednesday, September 14, 2011 1:07 PM Subject: Re: Numpy - MKL - build error Yes, they all are present in that directory. Also, I tried with root as login. -r-xr-xr-x 1 root root 26342559 Aug 9 22:19 libmkl_avx.so -r--r--r-- 1 root root 1190224 Aug 9 22:26 libmkl_blacs_ilp64.a -r--r--r-- 1 root root 1191496 Aug 9 22:25 libmkl_blacs_intelmpi_ilp64.a -r-xr-xr-x 1 root root 497597 Aug 9 22:25 libmkl_blacs_intelmpi_ilp64.so -r--r--r-- 1 root root 676206 Aug 9 22:21 libmkl_blacs_intelmpi_lp64.a -r-xr-xr-x 1 root root 267010 Aug 9 22:21 libmkl_blacs_intelmpi_lp64.so -r--r--r-- 1 root root 674926 Aug 9 22:22 libmkl_blacs_lp64.a -r--r--r-- 1 root root 1218290 Aug 9 22:28 libmkl_blacs_openmpi_ilp64.a -r--r--r-- 1 root root 703042 Aug 9 22:23 libmkl_blacs_openmpi_lp64.a -r--r--r-- 1 root root 1191152 Aug 9 22:29 libmkl_blacs_sgimpt_ilp64.a -r--r--r-- 1 root root 675854 Aug 9 22:23 libmkl_blacs_sgimpt_lp64.a -r--r--r-- 1 root root 425802 Aug 9 20:44 libmkl_blas95_ilp64.a -r--r--r-- 1 root root 421410 Aug 9 20:44 libmkl_blas95_lp64.a -r--r--r-- 1 root root 144354 Aug 9 22:29 libmkl_cdft_core.a -r-xr-xr-x 1 root root 115588 Aug 9 22:29 libmkl_cdft_core.so -r--r--r-- 1 root root 231886824 Aug 9 22:07 libmkl_core.a -r-xr-xr-x 1 root root 16730033 Aug 9 22:18 libmkl_core.so -r-xr-xr-x 1 root root 21474555 Aug 9 22:18 libmkl_def.so -r--r--r-- 1 root root 14974574 Aug 9 22:06 libmkl_gf_ilp64.a -r-xr-xr-x 1 root root 7008828 Aug 9 22:48 libmkl_gf_ilp64.so -r--r--r-- 1 root root 15140998 Aug 9 22:06 libmkl_gf_lp64.a -r-xr-xr-x 1 root root 7055304 Aug 9 22:48 libmkl_gf_lp64.so -r--r--r-- 1 root root 16435120 Aug 9 22:07 libmkl_gnu_thread.a -r-xr-xr-x 1 root root 9816940 Aug 9 22:49 libmkl_gnu_thread.so -r--r--r-- 1 root root 14968130 Aug 9 22:06 libmkl_intel_ilp64.a -r-xr-xr-x 1 root root 7008368 Aug 9 22:48 libmkl_intel_ilp64.so -r--r--r-- 1 root root 15134406 Aug 9 22:06 libmkl_intel_lp64.a -r-xr-xr-x 1 root root 7053588 Aug 9 22:48 libmkl_intel_lp64.so -r--r--r-- 1 root root 2472940 Aug 9 22:07 libmkl_intel_sp2dp.a -r-xr-xr-x 1 root root 1191479 Aug 9 22:20 libmkl_intel_sp2dp.so -r--r--r-- 1 root root 27642508 Aug 9 22:07 libmkl_intel_thread.a -r-xr-xr-x 1 root root 17516608 Aug 9 22:49 libmkl_intel_thread.so -r--r--r-- 1 root root 5350948 Aug 9 20:44 libmkl_lapack95_ilp64.a -r--r--r-- 1 root root 5413476 Aug 9 20:44 libmkl_lapack95_lp64.a -r-xr-xr-x 1 root root 29543829 Aug 9 22:19 libmkl_mc3.so -r-xr-xr-x 1 root root 25428037 Aug 9 22:19 libmkl_mc.so -r-xr-xr-x 1 root root 22888659 Aug 9 22:18 libmkl_p4n.so -r--r--r-- 1 root root 19232716 Aug 9 22:07 libmkl_pgi_thread.a -r-xr-xr-x 1 root root 12243062 Aug 9 22:49 libmkl_pgi_thread.so -r-xr-xr-x 1 root root 4984870 Aug 9 22:49 libmkl_rt.so -r--r--r-- 1 root root 10367758 Aug 9 22:49 libmkl_scalapack_ilp64.a -r-xr-xr-x 1 root root 6574928 Aug 9 22:50 libmkl_scalapack_ilp64.so -r--r--r-- 1 root root 10292432 Aug 9 22:49 libmkl_scalapack_lp64.a -r-xr-xr-x 1 root root 6452627 Aug 9 22:50 libmkl_scalapack_lp64.so -r--r--r-- 1 root root 9958444 Aug 9 22:07 libmkl_sequential.a -r-xr-xr-x 1 root root 5926347 Aug 9 22:48 libmkl_sequential.so -r--r--r-- 1 root root 1048 Aug 9 16:50 libmkl_solver_ilp64.a -r--r--r-- 1 root root 1048 Aug 9 16:50 libmkl_solver_ilp64_sequential.a -r--r--r-- 1 root root 1048 Aug 9 16:50 libmkl_solver_lp64.a -r--r--r-- 1 root root 1048 Aug 9 16:50 libmkl_solver_lp64_sequential.a -r-xr-xr-x 1 root root 6711968 Aug 9 22:48 libmkl_vml_avx.so -r-xr-xr-x 1 root root 2795928 Aug 9 22:47 libmkl_vml_def.so -r-xr-xr-x 1 root root 5476786 Aug 9 22:48 libmkl_vml_mc2.so -r-xr-xr-x 1 root root 5778052 Aug 9 22:48 libmkl_vml_mc3.so -r-xr-xr-x 1 root root 5382511 Aug 9 22:48 libmkl_vml_mc.so -r-xr-xr-x 1 root root 4235841 Aug 9 22:48 libmkl_vml_p4n.so drwxr-xr-x 3 root root 4096 Aug 18 11:43 locale Y you can reach the person managing the list at numpy-discussion-ow...@scipy.org Message: 1 Date: Tue, 13 Sep 2011 09:58:27 -0400 From: Olivier Delalleau sh...@keba.be Subject: Re: [Numpy-discussion] Numpy - MKL - build error To: Discussion of Numerical Python numpy-discussion@scipy.org Message-ID: cafxk4bpdn7qcwmze2g565gsuontau7lmh4fgsth_zy_r+nl...@mail.gmail.com Content-Type: text/plain; charset=iso-8859-1 Sorry if it sounds like a stupid question, but are the files listed in the error message present in that directory? If yes, maybe try running the command with sudo, just in case it would be some weird permission issue. -=- Olivier 2011/9/13 Igor Ying igor.y...@yahoo.com Hi, I am very new to Numpy and trying to build Numpy 1.6.1 with Intel MKL 10.3.6
[Numpy-discussion] behaviour of np.loadtxt
Dear list, I'm encountering a problem with np.loadtxt. Suppose i have a file containing three columns of data (and 10 rows), like: 0.001 0.003 0.005 0.001 0.003 0.006 0.002 0.004 0.002 0.004 0.002 0.007 0.001 0.003 0.006 0.002 0.004 0.002 0.004 0.002 0.007 0.001 0.003 0.006 0.002 0.004 0.002 0.004 0.002 0.007 If i give: len ( np.loadtxt( filename, unpack=True ) ) i get 3, since i have a three rows array. That's fine. If instead the file has a single column, i.e., 0.004 0.002 0.007 0.004 0.002 0.007 0.004 0.002 0.007 0.004 the command len ( np.loadtxt( filename, unpack=True ) ) returns 10, where i would expect it to return 1, to be consistent with the behaviour when there are multiple columns. Is there a reason for why it is not like that? Cheers Davide Lasagna ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy.test() failure
ERROR: test_polyfit (test_polynomial.TestDocs) -- Traceback (most recent call last): File /home/nwagner/local/lib64/python2.6/site-packages/numpy/lib/tests/test_polynomial.py, line 106, in test_polyfit weights = arange(8,1,-1)**2/7.0 NameError: global name 'arange' is not defined == FAIL: Tests polyfit -- Traceback (most recent call last): File /home/nwagner/local/lib64/python2.6/site-packages/numpy/ma/tests/test_extras.py, line 622, in test_polyfit assert_almost_equal(a, a_) File /home/nwagner/local/lib64/python2.6/site-packages/numpy/ma/testutils.py, line 155, in assert_almost_equal err_msg=err_msg, verbose=verbose) File /home/nwagner/local/lib64/python2.6/site-packages/numpy/ma/testutils.py, line 221, in assert_array_almost_equal header='Arrays are not almost equal') File /home/nwagner/local/lib64/python2.6/site-packages/numpy/ma/testutils.py, line 186, in assert_array_compare verbose=verbose, header=header) File /home/nwagner/local/lib64/python2.6/site-packages/numpy/testing/utils.py, line 677, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal (mismatch 100.0%) x: array([ 4.25134878, 1.14131297, 0.20519666, 0.01701 ]) y: array([ 1.9345248 , 0.49711011, 0.10202554, 0.00928034]) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy.test() failure
On Wed, Sep 14, 2011 at 10:45 PM, Samuel John sc...@samueljohn.de wrote: Hi Nils, which version of numpy, which os? Latest master. Due to https://github.com/numpy/numpy/commit/af22fc43 Travis, did you run the test suite? arange is used but not imported. Ralf I can infer that you use python 2.6 in 64bit, right? Right after the beginning of the numpy.test() are some crucial information. bests Samuel On 14.09.2011, at 22:09, Nils Wagner wrote: ERROR: test_polyfit (test_polynomial.TestDocs) -- Traceback (most recent call last): File /home/nwagner/local/lib64/python2.6/site-packages/numpy/lib/tests/test_polynomial.py, line 106, in test_polyfit weights = arange(8,1,-1)**2/7.0 NameError: global name 'arange' is not defined == FAIL: Tests polyfit -- Traceback (most recent call last): File /home/nwagner/local/lib64/python2.6/site-packages/numpy/ma/tests/test_extras.py, line 622, in test_polyfit assert_almost_equal(a, a_) File /home/nwagner/local/lib64/python2.6/site-packages/numpy/ma/testutils.py, line 155, in assert_almost_equal err_msg=err_msg, verbose=verbose) File /home/nwagner/local/lib64/python2.6/site-packages/numpy/ma/testutils.py, line 221, in assert_array_almost_equal header='Arrays are not almost equal') File /home/nwagner/local/lib64/python2.6/site-packages/numpy/ma/testutils.py, line 186, in assert_array_compare verbose=verbose, header=header) File /home/nwagner/local/lib64/python2.6/site-packages/numpy/testing/utils.py, line 677, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal (mismatch 100.0%) x: array([ 4.25134878, 1.14131297, 0.20519666, 0.01701 ]) y: array([ 1.9345248 , 0.49711011, 0.10202554, 0.00928034]) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy.test() failure
On Wed, Sep 14, 2011 at 2:45 PM, Samuel John sc...@samueljohn.de wrote: Hi Nils, which version of numpy, which os? I can infer that you use python 2.6 in 64bit, right? Right after the beginning of the numpy.test() are some crucial information. bests Samuel On 14.09.2011, at 22:09, Nils Wagner wrote: ERROR: test_polyfit (test_polynomial.TestDocs) -- Traceback (most recent call last): File /home/nwagner/local/lib64/python2.6/site-packages/numpy/lib/tests/test_polynomial.py, line 106, in test_polyfit weights = arange(8,1,-1)**2/7.0 NameError: global name 'arange' is not defined == FAIL: Tests polyfit -- Traceback (most recent call last): File /home/nwagner/local/lib64/python2.6/site-packages/numpy/ma/tests/test_extras.py, line 622, in test_polyfit assert_almost_equal(a, a_) File /home/nwagner/local/lib64/python2.6/site-packages/numpy/ma/testutils.py, line 155, in assert_almost_equal err_msg=err_msg, verbose=verbose) File /home/nwagner/local/lib64/python2.6/site-packages/numpy/ma/testutils.py, line 221, in assert_array_almost_equal header='Arrays are not almost equal') File /home/nwagner/local/lib64/python2.6/site-packages/numpy/ma/testutils.py, line 186, in assert_array_compare verbose=verbose, header=header) File /home/nwagner/local/lib64/python2.6/site-packages/numpy/testing/utils.py, line 677, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal (mismatch 100.0%) x: array([ 4.25134878, 1.14131297, 0.20519666, 0.01701 ]) y: array([ 1.9345248 , 0.49711011, 0.10202554, 0.00928034]) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion This is from Travis' push last night adding weights to polyfit. Travis, pretty much everything needs undergo review and testing before commits no matter how trivial it may look. Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] load from text files Pull Request Review
On 9/14/11 1:01 PM, Christopher Barker wrote: numpy.ndarray.resize is a different method, and I'm pretty sure it should be as fast or faster that np.empty + np.append. My profile: In [25]: %timeit f1 # numpy.resize() 1000 loops, best of 3: 163 ns per loop In [26]: %timeit f2 #numpy.ndarray.resize() 1000 loops, best of 3: 136 ns per loop In [27]: %timeit f3 # numpy.empty() + append() 1000 loops, best of 3: 136 ns per loop those last two couldn't b more identical! (though this is an excercise in unrequired optimization!) My test code: #!/usr/bin/env python test_resize A test of various numpy re-sizing options import numpy def f1(): numpy.resize l = 100 a = numpy.zeros((l,)) for i in xrange(1000): l += l a = numpy.resize(a, (l,) ) return None def f2(): numpy.ndarray.resize l = 100 a = numpy.zeros((l,)) for i in xrange(1000): l += l a.resize(a, (l,) ) return None def f3(): numpy.empty + append l = 100 a = numpy.zeros((l,)) for i in xrange(1000): b = np.empty((l,)) a.append(b) return None -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/ORR(206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] load from text files Pull Request Review
On Wed, Sep 14, 2011 at 4:25 PM, Christopher Barker chris.bar...@noaa.govwrote: On 9/14/11 1:01 PM, Christopher Barker wrote: numpy.ndarray.resize is a different method, and I'm pretty sure it should be as fast or faster that np.empty + np.append. My profile: In [25]: %timeit f1 # numpy.resize() 1000 loops, best of 3: 163 ns per loop In [26]: %timeit f2 #numpy.ndarray.resize() 1000 loops, best of 3: 136 ns per loop In [27]: %timeit f3 # numpy.empty() + append() 1000 loops, best of 3: 136 ns per loop those last two couldn't b more identical! (though this is an excercise in unrequired optimization!) Are you sure the f2 code works? a.resize() takes only a shape tuple. As coded, you should get an exception. Ben Root ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] load from text files Pull Request Review
On 9/14/11 2:41 PM, Benjamin Root wrote: Are you sure the f2 code works? a.resize() takes only a shape tuple. As coded, you should get an exception. wow, what an idiot! I think I just timed how long it takes to raise that exception... And when I fix that, I get a memory error. When I fix that, I find that f3() wasn't doing the right thing. What an astonishing lack of attention on my part! Here it is again, working, I hope! In [107]: %timeit f1() 10 loops, best of 3: 50.7 ms per loop In [108]: %timeit f2() 1000 loops, best of 3: 719 us per loop In [109]: %timeit f3() 100 loops, best of 3: 19 ms per loop So: numpy.resize() is the slowest numpy.empty+ numpy.append() is faster numpy.ndarray.resize() is the fastest Which matches my expectations, for once! -Chris The code: #!/usr/bin/env python test_resize A test of various numpy re-sizing options import numpy def f1(): numpy.resize extra = 100 l = extra a = numpy.zeros((l,)) for i in xrange(100): l += extra a = numpy.resize(a, (l,) ) return a def f2(): numpy.ndarray.resize extra = 100 l = extra a = numpy.zeros((l,)) for i in xrange(100): l += extra a.resize( (l,) ) return a def f3(): numpy.empty + append extra = 100 l = extra a = numpy.zeros((l,)) for i in xrange(100): b = numpy.empty((extra,)) a = numpy.append(a, b) return a a1 = f1() a2 = f2() a3 = f3() if a1.shape == a2.shape == a3.shape: print they are all returning the same size array else: print Something is wrong! -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/ORR(206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] load from text files Pull Request Review
On Wed, Sep 14, 2011 at 5:30 PM, Christopher Barker chris.bar...@noaa.gov wrote: On 9/14/11 2:41 PM, Benjamin Root wrote: Are you sure the f2 code works? a.resize() takes only a shape tuple. As coded, you should get an exception. wow, what an idiot! I think I just timed how long it takes to raise that exception... And when I fix that, I get a memory error. When I fix that, I find that f3() wasn't doing the right thing. What an astonishing lack of attention on my part! Here it is again, working, I hope! In [107]: %timeit f1() 10 loops, best of 3: 50.7 ms per loop In [108]: %timeit f2() 1000 loops, best of 3: 719 us per loop In [109]: %timeit f3() 100 loops, best of 3: 19 ms per loop So: numpy.resize() is the slowest numpy.empty+ numpy.append() is faster numpy.ndarray.resize() is the fastest Which matches my expectations, for once! Good catch! I didn't think the difference between np.resize and ndarray.resize would matter. (And I was getting inscrutable errors when I called ndarray.resize that told me to use np.resize instead.) -Chris JS -Chris The code: #!/usr/bin/env python test_resize A test of various numpy re-sizing options import numpy def f1(): numpy.resize extra = 100 l = extra a = numpy.zeros((l,)) for i in xrange(100): l += extra a = numpy.resize(a, (l,) ) return a def f2(): numpy.ndarray.resize extra = 100 l = extra a = numpy.zeros((l,)) for i in xrange(100): l += extra a.resize( (l,) ) return a def f3(): numpy.empty + append extra = 100 l = extra a = numpy.zeros((l,)) for i in xrange(100): b = numpy.empty((extra,)) a = numpy.append(a, b) return a a1 = f1() a2 = f2() a3 = f3() if a1.shape == a2.shape == a3.shape: print they are all returning the same size array else: print Something is wrong! -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/ORR (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Polynomial implementation in numpy.ma
Hi all, There were some failures in the polynomial tests earlier today, and while investigating I saw that numpy.ma implements its own root finder. It uses inversion of a Van der Monde matrix, which I believe may suffer from some numerical instability problems. Given that Charles has gone to some length to implement good polynomial root finders, I think it would be best to employ those instead, and simply pre-filter the data that comes from the masked array module, if possible. Regards Stéfan ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] 1.7.x release of NumPy
Hi all, Has there been a discussion of a 1.7.x release of NumPy? There are a few new features that should go into the 1.x release of NumPy, that don't require the ABI changes of 2.0.I thought I had heard Mark talk in support of such a thing. What are the plans for the release of NumPy 2.0? Between my experiences over the last 4 years of working on real-world projects, the data array summit, SciPy, and conversations with Mark Wiebe, Peter Wang, Wes McKinney and others over the past months, there is a collection of pretty concrete ideas emerging about where the next version of NumPy should go. Some of these can be folded into NumPy 2.0 (and even 1.7), but my mind is racing around NumPy 3.0 at this point. -Travis ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] 1.7.x release of NumPy
On Thu, Sep 15, 2011 at 5:32 AM, Travis Oliphant teoliph...@gmail.comwrote: Hi all, Has there been a discussion of a 1.7.x release of NumPy? There are a few new features that should go into the 1.x release of NumPy, that don't require the ABI changes of 2.0.I thought I had heard Mark talk in support of such a thing. A little. I think our 6-month release cycle is working pretty well, which means a 1.7.0 release around mid-November. There are definitely enough new features for a solid release already, but I have the impression that it wouldn't hurt for the missing data functionality to get exercised a bit more. What are the plans for the release of NumPy 2.0? Mark and others have been very good at maintaining ABI compatibility. At the same time the numpy-refactor branch has dropped off the radar (any news/plans?). So I'm wondering what would go into 2.0 that is ready now or soon? Ralf ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
