Closed clamus closed 10 years ago
dengemann
commented
10 years ago @lulopolar could you share the entire output of that test run? It would be good to now which exact tests segfault.
I am not sure if it helps to know that I run this on osx 10.8 with Canopy python.
I have used exactly that setup for a long time. Segfaults are not expected ...
larsoner
commented
10 years ago It would also be good to know which versions of numpy and scipy you are running. If the segfaulting test is in the permutation testing (it looks that way), those are the packages most heavily relied upon for those operations.
clamus
commented
10 years ago Here is the complete output. Also, I run this in a virtualenv created with the Canopy's venv command which they backported from python 3 as I understand (
(mnedev) Camilos-MacBook-Pro:mne-python lamexicana$ make rm -rf build find . -name ".pyc" | xargs rm -f find . -name ".so" | xargs rm -f find . -name ".pyd" | xargs rm -f rm -f tags python setup.py build_ext -i running build_ext Target needs sample data rm -f .coverage nosetests mne Test DICS with evoked data and single trials ... [Parallel(n_jobs=2)]: Done 1 out of 24 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=2)]: Done 14 out of 36 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 36 out of 60 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 40 out of 66 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 69 out of 85 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 80 out of 98 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 98 out of 98 | elapsed: 0.0s finished ok Test DICS source power computation ... [Parallel(n_jobs=2)]: Done 1 out of 18 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=2)]: Done 2 out of 20 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=2)]: Done 9 out of 25 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 10 out of 26 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 11 out of 27 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 19 out of 31 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 21 out of 33 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 22 out of 34 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 60 out of 98 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 80 out of 98 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 98 out of 98 | elapsed: 0.0s finished ok Test TF beamforming based on DICS ... [Parallel(n_jobs=2)]: Done 1 out of 41 | elapsed: 0.0s remaining: 0.3s [Parallel(n_jobs=2)]: Done 14 out of 76 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=2)]: Done 16 out of 78 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=2)]: Done 35 out of 90 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 36 out of 91 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 37 out of 95 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 40 out of 98 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 60 out of 98 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 80 out of 98 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 98 out of 98 | elapsed: 0.0s finished ok Test LCMV with evoked data and single trials ... [Parallel(n_jobs=2)]: Done 1 out of 90 | elapsed: 0.0s remaining: 0.8s [Parallel(n_jobs=2)]: Done 30 out of 160 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=2)]: Done 86 out of 216 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=2)]: Done 88 out of 218 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=2)]: Done 192 out of 318 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=2)]: Done 194 out of 322 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=2)]: Done 274 out of 456 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=2)]: Done 275 out of 457 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=2)]: Done 617 out of 769 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=2)]: Done 620 out of 773 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=2)]: Done 621 out of 774 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=2)]: Done 627 out of 782 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=2)]: Done 628 out of 783 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=2)]: Done 629 out of 785 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=2)]: Done 3381 out of 3381 | elapsed: 1.2s finished ok Test LCMV with raw data ... ok Test LCMV source power computation ... [Parallel(n_jobs=2)]: Done 1 out of 44 | elapsed: 0.0s remaining: 0.4s [Parallel(n_jobs=2)]: Done 12 out of 58 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 13 out of 59 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 27 out of 70 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 29 out of 72 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 30 out of 73 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 31 out of 74 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 32 out of 78 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 37 out of 95 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 38 out of 96 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 39 out of 97 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 40 out of 98 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 78 out of 130 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 266 out of 331 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=2)]: Done 268 out of 333 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=2)]: Done 269 out of 334 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=2)]: Done 329 out of 410 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=2)]: Done 330 out of 411 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=2)]: Done 1144 out of 1428 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=2)]: Done 1147 out of 1432 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=2)]: Done 1149 out of 1435 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=2)]: Done 1150 out of 1436 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=2)]: Done 1152 out of 1438 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=2)]: Done 1153 out of 1440 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=2)]: Done 1665 out of 2080 | elapsed: 0.6s remaining: 0.1s [Parallel(n_jobs=2)]: Done 1673 out of 2089 | elapsed: 0.6s remaining: 0.1s [Parallel(n_jobs=2)]: Done 1684 out of 2103 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=2)]: Done 1685 out of 2104 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=2)]: Done 1773 out of 2214 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=2)]: Done 1858 out of 2321 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=2)]: Done 1859 out of 2322 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=2)]: Done 1867 out of 2332 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=2)]: Done 1868 out of 2333 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=2)]: Done 1877 out of 2344 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=2)]: Done 2706 out of 3381 | elapsed: 1.2s remaining: 0.3s [Parallel(n_jobs=2)]: Done 3381 out of 3381 | elapsed: 1.4s finished ok Test TF beamforming based on LCMV ... ok Test Phase Slope Index (PSI) estimation ... ok Test frequency-domain connectivity methods ... [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 0 out of 0 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished 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when preload is False ... ok Test read bti config file ... ok Test read bti PDF file ... ok Test bti conversion to Raw object ... ok Test reading bti headshape ... ok Test reading raw bdf files ... ok Test reading raw edf files ... ok Test writing raw edf files when preload is False ... ok Test IO for hsp files ... ok Test IO for mrk files ... ok Test the coordinate transformation for hsp files ... ok Test reading raw kit files ... ok Test writing raw kit files when preload is False ... ok Test raw kit loc ... ok Test raw kit stim ch ... ok Test reading and writing hsp files ... ok Doctest: mne.fiff.pick.pick_channels_regexp ... ok Doctest: mne.fiff.tag.read_big ... ok Test compensation ... ok Test comensation by comparing with MNE ... ok Test IO for evoked data (fif + gz) with integer and str args ... ok Test for shifting of time scale ... ok Test for resampling of evoked data ... ok Test for detrending evoked data ... ok Test IO for multiple evoked datasets ... ok Test to_nitime ... SKIP: Skipping test: test_evoked_to_nitime: nitime not installed Test SSP proj operations ... ok Test evoked Pandas exporter ... ok Test fiducials i/o ... ok Test info object ... ok Test pick with regular expression ... ok Test CHPI position computation ... ok Test raw copying and appending combinations ... ok Test raw rank estimation ... ok Test saving and loading raw data using multiple formats ... ok Test loading multiple files simultaneously ... ok Test reading/writing of bad channels ... ok Test IO for raw data (Neuromag + CTF + gz) ... ok Test IO with complex data types ... ok Test getitem/indexing of Raw ... ok Test SSP proj operations ... ok Test preloading and modifying data ... ok Test raw Pandas exporter ... ok Test filtering (FIR and IIR) and Raw.apply_function interface ... 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SKIP: Skipping test: test_raw_to_nitime: nitime not installed Test index as time conversion ... ok Test time as index conversion ... ok Test saving raw ... ok Test with statement ... ok Test Raw compensation ... ok Test Raw compensation by comparing with MNE ... ok Test rereference eeg data ... ok Doctest: mne.filter.construct_iir_filter ... ok Doctest: mne.filter.detrend ... ok Doctest: mne.filter.is_power2 ... ok Doctest: mne.fixes._firwin2 ... ok Doctest: mne.fixes._matrix_rank ... ok Test converting forward solution between different representations ... ok Test IO for forward solutions ... ok Test projection of source space data to sensor space ... ok Test restriction of source space to source SourceEstimate ... ok Test restriction of source space to label ... ok Test averaging forward solutions ... ok Test making fwd using KIT, BTI, and CTF (compensated) files ... ok Test making M-EEG forward solution from python ... ok Test wrapping forward solution from python ... ok Doctest: mne.inverse_sparse.mxne_optim.prox_l1 ... ok Doctest: mne.inverse_sparse.mxne_optim.prox_l21 ... ok Test Gamma MAP inverse ... ok Test (TF-)MxNE inverse computation ... ok Test convergence of MxNE solver ... ok Test convergence of TF-MxNE solver ... ok Test equivalence of TF-MxNE (with alpha_time=0) and MxNE ... ok Test IO with .lout files ... ok Test IO with .lay files ... ok Test creation of EEG layout ... ok Test creation of grid layout ... ok Test finding layout ... ok Test MNE inverse warning without average EEG projection ... ok Test MNE inverse computation (precomputed and non-precomputed) ... ok Test MNE inverse computation (fixed orientation) ... ok Test MNE inverse computation (free orientation) ... ok Test MNE inverse computation with diagonal noise cov ... ok Test MNE inverse computation on volume source space ... ok Test IO of inverse_operator with GZip ... ok Test MNE with precomputed inverse operator on Raw ... ok Test MNE with fixed-orientation inverse operator on Raw ... ok Test MNE with precomputed inverse operator on Epochs ... ok Test MNE inverse computation given a mismatch of bad channels ... ok Test time freq with MNE inverse computation ... ok Test source PSD computation in label ... ok Test multi-taper source PSD computation in label from epochs ... ok Test find ECG peaks ... ok Test find EOG peaks ... ok Test additional ICA functionality ... ok Test ICA on raw and epochs ... ok Test recovery of full data when no source is rejected ... ok Test ICA data raw buffer rejection ... ok Test run_ica function ... ok Test the peak detection method ... ok Test computation of ECG SSP projectors ... ok Test computation of EOG SSP projectors ... ok Test computation of ExG projectors using parallelization ... [Parallel(n_jobs=2)]: Done 1 out of 3 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=2)]: Done 2 out of 3 | elapsed: 0.4s remaining: 0.2s [Parallel(n_jobs=2)]: Done 3 out of 3 | elapsed: 0.4s finished ok Test eliminate stim artifact ... ok Test the RtMockClient. ... ok Test emulation of realtime data stream. ... ok Test TCP/IP connection for StimServer <-> StimClient. ... ok Test simulation of evoked data ... ok Test generation of source estimate ... ok Test generation of sparse source estimate ... ok Test generation of source estimate ... ok Test generation of sparse source estimate ... ok Doctest: mne.stats.permutations.bin_perm_rep ... ok Test cluster level permutations tests ... [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.2s remaining: 0.2s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.2s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.2s remaining: 0.2s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.2s finished ok Test cluster level permutations T-test ... [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.1s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.1s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.1s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.1s finished ok Test cluster level permutations with connectivity matrix ... ok Test cluster level permutations with and without connectivity ... [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.1s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.1s remaining: 0.1s [Parallel(njobs=2)]: Done 2 out of 2 | elapsed: 0.1s finished make: ** [test] Segmentation fault: 11
On Nov 24, 2013, at 8:04 PM, "Denis A. Engemann" notifications@github.com wrote:
@lulopolar could you share the entire output of that test run? It would be good to now which exact tests segfault.
I am not sure if it helps to know that I run this on osx 10.8 with Canopy python.
I have used exactly that setup for a long time. Segfaults are not expected ...
Reply to this email directly or view it on GitHub.
dengemann
commented
10 years ago Mhmmm ... no bell ringing. Admittedly, I never tested our setup using venv ... could you try running tests from a regular install, just to reduce the number of unknowns ... I don't know whether this matters, but with recent numpy versions and mkl on mac I had problems with sklearn and joblib. Could you try running tests after setting the following variable (which forces the tests not to use parallel jobs) in your environment.
MNE_FORCE_SERIAL=1
clamus
commented
10 years ago @dengemann A related question about your setup for development for canopy python in mac. Do you just use the canopy "user" environment doing ln -s -path-to-mne-python/mne ~/Library/Enthought/Canopy_64bit/lib/python2.7/site-packages/mne?
dengemann
commented
10 years ago @lulopolar for the repos I would frequently pull from I always create a symlink into site-packages. So I don't run python setup.py install. Btw. currently I'm using Anaconda (mainly because I wanted to learn more about it and add explicit user support for it). For Unix open source software I''m using homebrew (hhttp://brew.sh/).
clamus
commented
10 years ago @dengemann Thanks for the recommendation on the Mac set up. I will try it tomorrow.
mluessi
commented
10 years ago Also, check if numpy and scipy work properly, e.g., use
import numpy; numpy.test()
import scipy; scipy.test()@dengemann I just ran the unit test on the standard "Canopy User" environment and I did not crash on the permutation test, but know I get and error where
clamus
commented
10 years ago @mluessi I run test for numpy and scipy. The do pass but I get some the following warnings. Are these harmless?
In [3]: numpy.test() Running unit tests for numpy NumPy version 1.8.0 NumPy is installed in /Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy Python version 2.7.3 | 64-bit | (default, Aug 8 2013, 05:37:06) [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] nose version 1.3.0 .........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S..............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................K.............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................K...........................................................................................................K...SK.S.......S.......................................................................................................................................................................................................................................................................................................................................................................................................................................................False .../Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/fft/tests/test_fft.py:52: ComplexWarning: Casting complex values to real discards the imaginary part a = a.astype(t1) ..../Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/fft/fftpack.py:325: ComplexWarning: Casting complex values to real discards the imaginary part a = asarray(a).astype(float)
Ran 4950 tests in 107.035s
OK (KNOWNFAIL=5, SKIP=6)
Out[3]:
In [4]: import scipy
In [5]: scipy.test()
Running unit tests for scipy
NumPy version 1.8.0
NumPy is installed in /Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy
SciPy version 0.13.0
SciPy is installed in /Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy
Python version 2.7.3 | 64-bit | (default, Aug 8 2013, 05:37:06) [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]
nose version 1.3.0
/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/lib/utils.py:134: DeprecationWarning: scipy.lib.blas is deprecated, use scipy.linalg.blas instead!
warnings.warn(depdoc, DeprecationWarning)
/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/lib/utils.py:134: DeprecationWarning: scipy.lib.lapack is deprecated, use scipy.linalg.lapack instead!
warnings.warn(depdoc, DeprecationWarning)
..............................................................................................................................................................................................................................K..................................................................................................................K.......................................................................................K..K......................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................SSSSSS......SSSSSS......SSSS......................................................................................0-th dimension must be fixed to 3 but got 15
..0-th dimension must be fixed to 3 but got 5
.......................S..........K..............................................................................................................................................................................................................................................................................................K.......................................................................................................................................................................................K................................................................................................................................................................/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/core/_methods.py:55: RuntimeWarning: Mean of empty slice.
warnings.warn("Mean of empty slice.", RuntimeWarning)
.................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................K.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................SSSSSSSSSSS.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................SS....................SSSSSSSSSS.......KKKSSS.S....KKK......S..K...KKK...KKK...............S..............SSSSS..SSSS.................................SS...............SSS.SSSSSSSSSS....SSSKKKSSS.S.SSSKKK......S...SSSKKKSSSKKK...........SSS.S..............SSSSS..SSSS.................................................................................KKKK..........KKKK...............KKKK....KKKK.....................................................................................................................................................................................................................................................................................................................................................................................................................KKKK..........KKKK...............KKKK....KKKK......................................................................................................................................................................................................................................................................................................................................................................SS............SSS.SSSSSSSSSS....SSSKKKSSS.S.SSSKKK......S...SSSKKKSSSKKK......S....SSS.S..............SSSSS..SSSS...............................K..S.SSS..........SSS.KKKKSKSSKS....SSSKKKSSS...SSSKKK..........SSSKKKSSSKKK......S.....SSS.S.........K.....KSSS...KSKS................................................SSS................SSSKKK......SSSKKK...........SSSKKK...SSSKKK......S....SSS............K................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/special/_testutils.py:244: RuntimeWarning: invalid value encountered in greater
pinf_x = np.isinf(x) & (x > 0)
/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/special/_testutils.py:245: RuntimeWarning: invalid value encountered in greater
pinf_y = np.isinf(y) & (y > 0)
/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/special/_testutils.py:246: RuntimeWarning: invalid value encountered in less
minf_x = np.isinf(x) & (x < 0)
/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/special/_testutils.py:247: RuntimeWarning: invalid value encountered in less
minf_y = np.isinf(y) & (y < 0)
.............................................................................................................................................................................................................................................................................................................................................................................................................................................................................../Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/core/_methods.py:77: RuntimeWarning: Degrees of freedom <= 0 for slice
warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning)
.............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................../Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/stats/distributions.py:7208: RuntimeWarning: invalid value encountered in greater_equal
return where(temp >= q, vals1, vals)
.............................................................................................../Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/stats/distributions.py:7584: RuntimeWarning: invalid value encountered in greater_equal
return where((temp >= q), vals1, vals)
............................./Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/stats/distributions.py:7357: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
return (1-p)(k-1) * p
/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/stats/distributions.py:7357: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
return (1-p)(k-1) * p
..../Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/stats/distributions.py:7525: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
return -pk * 1.0 / k / log(1 - p)
/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/stats/distributions.py:7525: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
return -pk * 1.0 / k / log(1 - p)
/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/stats/distributions.py:7525: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
return -pk * 1.0 / k / log(1 - p)
/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/stats/distributions.py:7525: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
return -pk * 1.0 / k / log(1 - p)
............S..........................................................................................S............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................./Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/ma/core.py:778: RuntimeWarning: invalid value encountered in absolute
return umath.absolute(a) * self.tolerance >= umath.absolute(b)
Ran 8661 tests in 155.546s
OK (KNOWNFAIL=114, SKIP=203)
Out[5]:
dengemann
commented
10 years ago @dengemann I just ran the unit test on the standard "Canopy User" environment and I did not crash on the permutation test
good to know, I suspected there might be an issue, admittedly not well understood.
but know I get and error where /mne/examples/MNE-sample-data/subjects/fsaverage/bem/fsaverage-ico-5-src.fif' is missing. The bem directory in fsaverage is empty in my case. Where can I find the MNE-sample-data that actually has this file?
You need to download the sample data.
The easiest way is to run one of the examples, e.g.
https://github.com/mne-tools/mne-python/blob/master/examples/plot_from_raw_to_epochs_to_evoked.py
The data will be automatically downloaded and extracted from the archive.
dengemann
commented
10 years ago @lulopolar I was assuming you did not aleady download the sample data. But reading more carefully you're saying a tests complain about single files missing.
Take a look here. These are additional dataset related scripts and tools we use to create the full sample data and update the sample data.
https://github.com/mne-tools/mne-scripts/tree/master/sample-data
dengemann
commented
10 years ago @lulopolar finally ... don't bother about the numpy scipy warnings at this point.
clamus
commented
10 years ago Hi Guys, I am back from a small holiday. I did run the shell scrip (run_meg_tutorial) to get the complete sample dataset processed after downloading it via plot_from_raw_to_epochs_to_evoked. However, when running the unit test I get a new error in test_make_forward. As I mentioned before, my setup is canopy 64 for osx, bumpy 1.8.0, scipy 0.13.0, and I have MNE-2.7.4-3373-MacOSX-i386. Any help will be greatly appreciated. Below is the last portion (which contains the error) of the terminal's output.
Traceback (most recent call last): File "/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/nose/case.py", line 197, in runTest self.test(_self.arg) File "/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/testing/decorators.py", line 146, in skipper_func return f(_args, **kwargs) File "/Users/lamexicana/repos/mne-python/mne/forward/tests/test_make_forward.py", line 173, in test_make_forward_solution _compare_forwards(fwd, fwd_py, 366, 22494) File "/Users/lamexicana/repos/mne-python/mne/forward/tests/test_make_forward.py", line 65, in _compare_forwards rtol=meg_rtol, atol=meg_atol) File "/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/testing/utils.py", line 1181, in assert_allclose verbose=verbose, header=header) File "/Users/lamexicana/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/testing/utils.py", line 644, in assert_array_compare raise AssertionError(msg) AssertionError: Not equal to tolerance rtol=0.0001, atol=1e-09
(mismatch 100.0%) x: array([[ -9.21401409e-07, -1.57427876e-05, -1.83606262e-05, ..., -1.77131619e-06, -4.09350741e-06, 1.33059129e-05], [ -1.71266292e-05, 1.36744693e-05, 1.16496294e-05, ...,... y: array([[ -9.21466405e-07, -1.57428742e-05, -1.83607100e-05, ..., -1.77136169e-06, -4.09359669e-06, 1.33059901e-05], [ -1.71268028e-05, 1.36744814e-05, 1.16497391e-05, ...,... -------------------- >> begin captured stdout << --------------------- Source space : /Users/lamexicana/shared/mne-python-datasets/MNE-sample-data/subjects/sample/bem/sample-oct-6-src.fif MRI -> head transform source : /Users/lamexicana/shared/mne-python-datasets/MNE-sample-data/MEG/sample/sample_audvis_raw-trans.fif Measurement data : /Users/lamexicana/repos/mne-python/mne/forward/tests/../../fiff/tests/data/test_raw.fif BEM model : /Users/lamexicana/shared/mne-python-datasets/MNE-sample-data/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif Accurate field computations Do computations in head coordinates Free source orientations Destination for the solution : None
Reading /Users/lamexicana/shared/mne-python-datasets/MNE-sample-data/subjects/sample/bem/sample-oct-6-src.fif... Read 2 source spaces a total of 8196 active source locations
Coordinate transformation: MRI (surface RAS) -> head 0.999310 0.009985 -0.035787 -3.17 mm 0.012759 0.812405 0.582954 6.86 mm 0.034894 -0.583008 0.811716 28.88 mm 0.000000 0.000000 0.000000 1000.00 mm
Read 306 MEG channels from test_raw.fif Coordinate transformation: MEG device -> head 0.991420 -0.039936 -0.124467 -6.13 mm 0.060661 0.984012 0.167456 0.06 mm 0.115790 -0.173570 0.977991 64.74 mm 0.000000 0.000000 0.000000 1000.00 mm Read 60 EEG channels from test_raw.fif 57 coil definitions read Head coordinate coil definitions created. Source spaces are now in head coordinates.
Setting up the BEM model using /Users/lamexicana/shared/mne-python-datasets/MNE-sample-data/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif...
Loading surfaces... Three-layer model surfaces loaded.
Loading the solution matrix...
Loaded linear collocation BEM solution from /Users/lamexicana/shared/mne-python-datasets/MNE-sample-data/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif Employing the head->MRI coordinate transform with the BEM model. BEM model sample-5120-5120-5120-bem-sol.fif is now set up
Source spaces are in head coordinates. Checking that the sources are inside the bounding surface and at least 5.0 mm away (will take a few...) 2 source space points omitted because they are outside the inner skull surface. 364 source space points omitted because of the 5.0-mm distance limit. 1 source space point omitted because it is outside the inner skull surface. 331 source space point omitted because of the 5.0-mm distance limit. Thank you for waiting.
Setting up compensation data... No compensation set. Nothing more to do.
Composing the field computation matrix... Computing MEG at 7498 source locations (free orientations)... Computing EEG at 7498 source locations (free orientations)...
Finished. Reading forward solution from /Users/lamexicana/shared/mne-python-datasets/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif... Reading a source space... Computing patch statistics... Patch information added... Distance information added... [done] Reading a source space... Computing patch statistics... Patch information added... Distance information added... [done] 2 source spaces read Desired named matrix (kind = 3523) not available Read MEG forward solution (7498 sources, 306 channels, free orientations) Desired named matrix (kind = 3523) not available Read EEG forward solution (7498 sources, 60 channels, free orientations) MEG and EEG forward solutions combined Source spaces transformed to the forward solution coordinate frame Cartesian source orientations... [done] nuse ntri np type id subject_his_id nn rr nuse_tri coord_frame tris nuse ntri np type id subject_his_id nn rr nuse_tri coord_frame tris working_dir command_line nchan source_nn source_rr source_ori surf_ori coord_frame nsource check MEG
--------------------- >> end captured stdout << ----------------------
raise AssertionError('\nNot equal to tolerance rtol=0.0001, atol=1e-09\n\n(mismatch 100.0%)\n x: array([[ -9.21401409e-07, -1.57427876e-05, -1.83606262e-05, ...,\n -1.77131619e-06, -4.09350741e-06, 1.33059129e-05],\n [ -1.71266292e-05, 1.36744693e-05, 1.16496294e-05, ...,...\n y: array([[ -9.21466405e-07, -1.57428742e-05, -1.83607100e-05, ...,\n -1.77136169e-06, -4.09359669e-06, 1.33059901e-05],\n [ -1.71268028e-05, 1.36744814e-05, 1.16497391e-05, ...,...')
mne 41 1 98% 80
mne.baseline 37 6 84% 52, 74-75, 79-81
mne.beamformer 2 0 100%
mne.beamformer._dics 179 7 96% 115, 326, 385, 495, 534-536
mne.beamformer._lcmv 213 6 97% 152, 428, 526, 667-669
mne.commands 0 0 100%
mne.commands.utils 18 13 28% 18-39
mne.connectivity 3 0 100%
mne.connectivity.effective 35 0 100%
mne.connectivity.spectral 472 29 94% 31, 34, 37, 40, 307, 328-330, 382-384, 403, 410, 469, 476, 482, 492, 498, 733, 743, 750-751, 811, 816, 830, 892-894, 917, 929, 1017, 1021
mne.connectivity.utils 14 2 86% 11, 14
mne.coreg 482 56 88% 93-96, 100-103, 108-110, 114-116, 121-125, 127-130, 139-141, 301-303, 346-348, 359, 370-372, 403-405, 531-534, 572-574, 585-587, 622, 686, 726, 750-752, 759, 764, 787, 797, 841, 895-900, 924-925, 983-985, 995, 999-1001, 1005-1006, 1023, 1027-1028
mne.cov 318 21 93% 28, 31, 79-81, 105, 109, 117-119, 173, 267, 351, 363, 371, 373, 444, 476-479, 619, 684
mne.cuda 163 112 31% 9-10, 43-102, 154-188, 215-222, 278-314, 359-384
mne.data 0 0 100%
mne.datasets 3 0 100%
mne.datasets.megsim 1 0 100%
mne.datasets.megsim.megsim 64 56 13% 64-126, 178-189
mne.datasets.megsim.urls 25 9 64% 150-160
mne.datasets.sample 1 0 100%
mne.datasets.sample.sample 10 0 100%
mne.datasets.spm_face 1 0 100%
mne.datasets.spm_face.spm_data 12 1 92% 16
mne.datasets.utils 93 38 59% 59, 79, 82, 97, 106-141, 145-154, 165-166
mne.decoding 4 0 100%
mne.decoding.classifier 115 5 96% 349, 351, 385, 394, 416
mne.decoding.csp 87 11 87% 89, 95-96, 99, 110-111, 119-120, 126-129, 195
mne.decoding.mixin 5 1 80% 27
mne.dipole 17 1 94% 39
mne.epochs 823 88 89% 61-63, 71, 73, 76, 78, 103, 107, 145-169, 175, 179, 183-186, 199, 273, 278, 280, 298, 308, 359, 396, 636, 663, 706, 718, 725, 903, 912, 917, 949, 970, 975-987, 1019, 1047, 1052, 1054-1056, 1059, 1061-1063, 1108, 1305-1306, 1382, 1384, 1436, 1439, 1441, 1444, 1491, 1507, 1633-1634, 1638-1639, 1658-1659, 1684, 1687-1688, 1743
mne.event 281 31 89% 48, 57, 101, 116-118, 130, 141-142, 154, 216, 226, 355, 358-360, 379-381, 410-411, 425, 537, 599-602, 635, 671, 674, 696, 699, 710
mne.fiff 10 0 100%
mne.fiff.brainvision 1 0 100%
mne.fiff.brainvision.brainvision 270 25 91% 137, 159, 174-175, 228, 338, 344-349, 363-366, 389, 392, 396-399, 403, 406, 410-415
mne.fiff.bti 1 0 100%
mne.fiff.bti.constants 74 0 100%
mne.fiff.bti.raw 599 92 85% 78, 260, 319, 340, 363-369, 405-412, 441-459, 634-642, 647-648, 663-669, 675-691, 696-704, 710-755, 773, 850, 913, 969, 1014, 1017-1018, 1112-1127
mne.fiff.bti.read 59 4 93% 49, 54, 59, 84
mne.fiff.bti.transforms 40 0 100%
mne.fiff.channels 12 0 100%
mne.fiff.compensator 57 5 91% 17, 45, 48, 57, 113
mne.fiff.constants 545 0 100%
mne.fiff.cov 88 8 91% 46, 58, 63, 73, 80, 101-103
mne.fiff.ctf 131 17 87% 18, 48-53, 59, 67, 73, 80, 86, 93, 98, 137, 160, 180, 183, 193, 196
mne.fiff.diff 26 22 15% 15-39
mne.fiff.edf 1 0 100%
mne.fiff.edf.edf 318 54 83% 82, 155, 174, 196-198, 217-230, 238, 245-247, 354, 367, 374-377, 381, 388-389, 393-395, 485-486, 501, 504, 533-552
mne.fiff.evoked 323 48 85% 94, 103-104, 108-109, 116-117, 144-145, 152, 172-180, 183, 187-202, 220-221, 237-238, 247-250, 254-255, 298-302, 466-467, 634-635
mne.fiff.kit 5 0 100%
mne.fiff.kit.constants 62 0 100%
mne.fiff.kit.coreg 88 17 81% 58-64, 69-71, 90-92, 100-101, 122-124, 126-128, 166-167
mne.fiff.kit.kit 327 23 93% 117-119, 220, 301, 312, 347, 388-395, 433, 485-486, 498, 533, 537, 552, 560, 578-580, 614-616
mne.fiff.matrix 61 12 80% 56-59, 64, 71, 76, 105-108, 120, 126
mne.fiff.meas_info 316 21 93% 103-105, 190, 192, 197, 199, 262, 265, 268, 271, 296, 320-322, 339, 348-351, 355-357, 478
mne.fiff.open 101 8 92% 65, 68, 71, 76, 132, 168-169, 191
mne.fiff.pick 188 36 81% 39, 46-65, 184, 188, 194, 219, 221, 227, 229, 231, 233, 279, 319, 325, 433, 453-456, 493
mne.fiff.proj 235 15 94% 99-101, 224, 230, 243, 249, 255, 261, 271, 274, 366, 372, 392, 548, 604
mne.fiff.raw 800 75 91% 143-144, 187-189, 192, 223-225, 237-238, 244, 253-257, 263, 272-273, 283-287, 344, 363, 368, 374, 442, 446, 586, 593, 599, 603, 720, 724, 728, 782, 855, 857, 859, 954, 960, 996-999, 1224, 1232, 1494-1495, 1545, 1562, 1601, 1603, 1605, 1654-1661, 1689, 1728, 1732, 1927, 1930, 1944-1947, 1960, 1979, 1991, 1993, 1995, 2000, 2002-2005, 2072, 2076, 2087
mne.fiff.tag 250 42 83% 42-47, 50-59, 118, 152, 154, 156, 159, 230, 247, 256, 264-275, 288, 300-305, 316, 323, 326, 332, 359-363, 446-449, 456
mne.fiff.tree 93 2 98% 20-21
mne.fiff.write 206 4 98% 69-71, 319
mne.filter 442 39 91% 76, 106, 109, 113, 477, 481, 494, 500, 527, 529, 616, 711, 720, 732, 735-739, 897, 1015-1022, 1096, 1169, 1233, 1236, 1240, 1266-1268, 1300, 1306, 1316, 1343-1344, 1359
mne.fixes 202 55 73% 38, 43-44, 51-52, 63-68, 98, 113, 123, 144, 149, 162, 175, 185, 195-196, 200, 204, 216-220, 225-226, 232-236, 243, 249, 350, 353, 358, 361, 364, 367, 377-378, 397, 408, 490-498, 503, 508, 526
mne.forward 2 0 100%
mne.forward._compute_forward 170 4 98% 27, 50, 194, 287
mne.forward._make_forward 271 34 87% 51, 63, 65, 78, 88, 105, 117, 120, 124, 134, 155, 225, 229-232, 236, 241, 243, 245, 248, 285, 299, 347-349, 367, 408, 415, 419, 427, 472-473, 489-492
mne.forward.forward 797 129 84% 62-65, 97, 102, 141, 172-173, 178-179, 184-185, 190-191, 199-202, 208, 215-216, 219-223, 249-250, 261-262, 290-291, 297, 302-303, 308-311, 330, 340-346, 394-395, 400-401, 406-408, 421-422, 433, 443, 453-455, 460-461, 466-470, 497, 504-505, 510, 580-582, 623-625, 635-636, 697-698, 708, 715, 723, 749-754, 773-778, 789, 825, 828-829, 872, 876, 881, 897, 904-915, 931, 941-942, 1020, 1096, 1161, 1246, 1367, 1388, 1390, 1419-1420, 1433, 1441, 1466, 1471, 1483, 1487, 1489, 1491, 1541, 1559, 1565, 1568, 1578-1582
mne.gui 21 17 19% 15-18, 52-56, 77-80, 86-89
mne.gui._coreg_gui 738 318 57% 30-62, 181-182, 212, 291, 307, 316-318, 363-364, 477-511, 521-523, 527, 580-587, 780-806, 810-813, 817-818, 822, 826, 830, 834-836, 840-842, 846-847, 850-852, 855-857, 860-862, 865-867, 870-872, 875-877, 880-890, 893, 896, 899, 902, 905, 908, 911, 915-924, 927-982, 985-986, 989, 992-994, 997-998, 1001, 1004-1005, 1008, 1011, 1014, 1017, 1020, 1023, 1026, 1054-1055, 1059, 1063-1068, 1072-1092, 1152, 1157, 1230, 1233-1234, 1237-1238, 1241, 1244-1254, 1258-1331, 1336, 1340-1345, 1348-1349, 1352, 1356-1364, 1369-1372, 1375
mne.gui._fiducials_gui 270 138 49% 22-43, 119, 121, 134, 137-139, 153, 157, 179, 196-198, 249-250, 253, 256, 259-279, 282-330, 334-342, 381, 384-386, 389, 407-415, 419-453, 456-462
mne.gui._file_traits 220 62 72% 20-42, 87-99, 106-118, 124, 160-161, 291, 356, 361-366, 378, 389-391, 417-430
mne.gui._kit2fiff_gui 297 134 55% 26-51, 66-68, 145-148, 159, 171-174, 188-191, 195, 207-210, 216, 223-235, 255-258, 262-264, 270-279, 285, 300-308, 392-438, 441, 445-448, 451, 455-485, 511, 514, 517
mne.gui._marker_gui 249 109 56% 22-47, 57-62, 115-131, 182-184, 192, 218, 222-247, 251, 258, 267-269, 273, 275, 282-284, 295-303, 323, 330, 338-345, 383, 386, 389, 392-415, 432, 435
mne.gui._viewer 207 151 27% 21-47, 91-97, 101-140, 161-165, 170-190, 209-210, 213-223, 227-240, 244-245, 252-272, 277-281, 303-307, 312-340
mne.inverse_sparse 2 0 100%
mne.inverse_sparse._gamma_map 118 7 94% 75, 120, 124, 130, 153, 158, 264
mne.inverse_sparse.mxne_debiasing 45 1 98% 128
mne.inverse_sparse.mxne_inverse 177 10 94% 41, 48, 165, 191-193, 220, 251, 264, 405
mne.inverse_sparse.mxne_optim 307 11 96% 24, 26, 33, 64, 198, 317-318, 328-331, 385, 623
mne.label 538 91 83% 72, 75, 85-87, 140, 154, 165-167, 317, 319, 328, 332, 349, 378, 386-389, 400-406, 438, 451, 535, 559, 573, 576, 617, 624, 641, 685-688, 733-759, 765-777, 828, 832, 840, 882-892, 900, 903-914, 920, 1182, 1204
mne.layouts 1 0 100%
mne.layouts.layout 247 12 95% 67, 160, 205, 208, 263, 274, 363, 366, 378, 415, 505, 518
mne.minimum_norm 2 0 100%
mne.minimum_norm.inverse 562 97 83% 46, 81-82, 90-91, 100-101, 108-109, 115-116, 125-126, 137-144, 150-151, 161-162, 172-173, 176, 205, 220-221, 226-230, 246-247, 274-276, 347-351, 382, 390, 426, 428, 447, 454, 457, 484, 503, 523, 571-573, 635, 640, 659-660, 676, 683-685, 687-689, 691-693, 696, 852, 931-937, 1031-1047, 1177-1179, 1193, 1200, 1202, 1208, 1211, 1346
mne.minimum_norm.time_frequency 238 12 95% 415, 435-436, 447, 503, 507, 527-529, 532, 570-572, 582
mne.misc 61 18 70% 28-29, 42, 66-67, 85-100
mne.mixed_norm 5 0 100%
mne.parallel 67 24 64% 16, 63-68, 79-83, 93-96, 121-122, 125-128, 141-144
mne.preprocessing 5 0 100%
mne.preprocessing.ecg 78 4 95% 64, 162, 168, 172
mne.preprocessing.eog 45 12 73% 48-68
mne.preprocessing.ica 570 46 92% 185, 197, 227, 292-293, 297, 318, 330, 378, 385, 389-390, 394, 501-503, 666-668, 717-730, 885, 944, 951, 1147-1148, 1178, 1186, 1236, 1337, 1340, 1351, 1364, 1482-1486, 1490-1491, 1584
mne.preprocessing.maxfilter 104 70 33% 46, 72, 88, 195-292
mne.preprocessing.peak_finder 82 18 78% 49, 89-91, 96-98, 117, 138-140, 151-157, 166
mne.preprocessing.ssp 83 18 78% 24-25, 109, 140, 144-145, 155, 158, 161, 164, 166-177
mne.preprocessing.stim 26 1 96% 40
mne.proj 152 15 90% 62-63, 65-66, 68-69, 127, 131, 142, 277, 281, 285, 289, 318, 349
mne.realtime 4 0 100%
mne.realtime.client 164 132 20% 41-60, 65-71, 99-130, 146-168, 181-197, 207-230, 240, 250-260, 264-265, 269, 282-287, 297-302, 313-314, 319-320, 324-325, 340-350, 365-370
mne.realtime.epochs 144 22 85% 158, 220-225, 242, 251, 315, 323, 328, 359, 363, 367-369, 377, 387, 390-395
mne.realtime.mockclient 50 3 94% 159, 171, 175
mne.realtime.stim_server_client 111 7 94% 81, 253-254, 278-279, 288-289
mne.selection 38 5 87% 47, 57, 77-79, 95
mne.simulation 2 0 100%
mne.simulation.evoked 34 2 94% 116, 118
mne.simulation.source 78 3 96% 75, 90, 152
mne.source_estimate 948 94 90% 56, 254-257, 263-266, 275-278, 291, 343, 358, 404, 408, 414, 418, 428, 432, 474, 620, 623, 629, 638, 737, 902, 904, 977, 1003, 1016, 1030, 1093, 1109, 1112, 1136, 1138, 1267, 1282-1283, 1286, 1368-1375, 1492, 1522, 1529, 1586, 1689, 1697, 1717-1720, 1768, 1826, 1859-1864, 1920-1921, 1927, 1929, 1979, 2026, 2029, 2032, 2035, 2039, 2074, 2086, 2088, 2127-2129, 2186, 2237, 2260, 2285-2292, 2323, 2355, 2393-2394, 2413, 2417, 2439, 2451, 2484, 2486, 2517
mne.source_space 891 290 67% 68, 73, 97, 163, 236, 246, 265, 284, 288, 304, 316, 320, 327, 331, 335, 339, 346, 350, 353, 360-362, 367, 371, 491, 513, 592, 624-627, 639, 645, 707, 711, 714, 717, 740, 767, 776-778, 801, 849, 865, 875, 879, 1019, 1022-1025, 1031, 1037, 1041, 1043-1054, 1063, 1066-1069, 1078-1080, 1082, 1084, 1094-1116, 1120, 1123, 1131, 1137-1146, 1167, 1173, 1189-1351, 1355, 1361-1449, 1457, 1466-1469, 1582, 1584, 1586, 1590, 1626
mne.stats 4 0 100%
mne.stats.cluster_level 554 77 86% 106, 180-182, 198, 212-217, 241, 315, 346, 381, 383, 395, 415-422, 435, 441, 454, 458, 465, 469, 498, 518, 541, 557-570, 586, 605, 610-614, 629-640, 676, 686, 696, 720, 796-799, 835, 873, 991, 1121, 1248, 1361, 1402-1407, 1422-1423, 1427-1433
mne.stats.multi_comp 33 0 100%
mne.stats.parametric 77 1 99% 261
mne.stats.permutations 48 0 100%
mne.surface 637 139 78% 65-66, 74-75, 92, 122, 138-139, 145-146, 154, 164-165, 169-170, 176-179, 183-184, 188-189, 218, 226, 237, 244, 248, 254, 262, 265, 419, 423, 426, 430, 438-451, 485-488, 525-527, 532-542, 567-588, 598, 617-620, 671, 760-768, 771, 820, 860, 874, 877-878, 956-959, 968-983, 990, 1012, 1015, 1022-1040, 1085-1091
mne.time_frequency 6 0 100%
mne.time_frequency.ar 43 9 79% 54, 63, 65, 75, 148-152
mne.time_frequency.csd 108 12 89% 48-51, 116, 125, 131, 177-179, 202-206
mne.time_frequency.multitaper 166 16 90% 45, 59, 91, 157-160, 220, 224, 285, 288, 347, 489, 514-516, 531
mne.time_frequency.psd 53 3 94% 53, 60, 132
mne.time_frequency.stft 87 12 86% 44, 47, 54, 57, 62, 66, 135, 139, 142, 146, 149, 153
mne.time_frequency.tfr 155 22 86% 50, 61, 106, 112, 120-122, 143, 146-148, 188, 325-327, 332, 387-396
mne.transforms 181 58 68% 46, 214, 221-237, 266, 304, 348, 389-461
mne.utils 532 72 86% 53, 165, 201-205, 246, 390, 407, 412-413, 419, 451-452, 457, 465-466, 480-482, 485-486, 502-503, 506-507, 523-524, 527-528, 542-543, 574-576, 631, 721, 742-745, 758-765, 811, 820-821, 854, 858, 869-870, 880, 960, 1009, 1120-1126, 1139, 1141-1144, 1146-1149, 1153, 1178, 1186, 1199, 1204, 1210, 1220-1221
Ran 302 tests in 1174.559s
FAILED (SKIP=3, failures=1) make: *\ [test] Error 1
dengemann
commented
10 years ago @lulopolar welcome back;-) This could be a 32bit/ 64bit precision issue -- could you try using the 64bit nightly build instead?
agramfort
commented
10 years ago you could also try to update the sample data that was updated for the release last week.
dengemann
commented
10 years ago you could also try to update the sample data that was updated for the release last week.
... and make sure your master is up to date.
I am trying to fork the repository for development. I follow the instruction at http://martinos.org/mne/contributing.html but get a segmentation fault doing the unit tests. I am not sure if it helps to know that I run this on osx 10.8 with Canopy python. I go to the mne-python directory and type 'make'. This is the last message that I get:
Test cluster level permutations with connectivity matrix ... ok Test cluster level permutations with and without connectivity ... [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.0s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.0s remaining: 0.0s
"More messages identical to this"
[Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.2s finished [Parallel(n_jobs=2)]: Done 1 out of 2 | elapsed: 0.2s remaining: 0.2s [Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 0.2s finished make: *\ [test] Segmentation fault: 11
I also have the automatically generated problem report, in case that might be of any help. Any ideas how to get this working?