python-mne fails to build with pytest 8. =================================== FAILURES =================================== ________________________________ test_maxfilter ________________________________ mne/commands/tests/test_commands.py:224: in test_maxfilter with pytest.warns(RuntimeWarning, match="Don't use"): E FutureWarning: NOTE: apply_maxfilter() is a deprecated function. apply_maxfilter will be removed in 1.7, use mne.preprocessing.maxwell_filter or the MEGIN command-line utility maxfilter and mne.bem.fit_sphere_to_headshape instead.. _____________________________ test_magnetic_dipole _____________________________ mne/forward/tests/test_make_forward.py:203: in test_magnetic_dipole with pytest.warns(RuntimeWarning, match="Coil too close"): E RuntimeWarning: invalid value encountered in divide ----------------------------- Captured stdout call ----------------------------- Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle ____________________________ test_vhdr_versions[\n] ____________________________ mne/io/brainvision/tests/test_brainvision.py:256: in test_vhdr_versions with pytest.warns(RuntimeWarning, match="Missing header"): E RuntimeWarning: MNE-Python currently only supports header versions 1.0 and 2.0, got unparsable ''. Contact MNE-Python developers for support. ----------------------------- Captured stdout call ----------------------------- Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test.vhdr... Setting channel info structure... Extracting parameters from /tmp/pytest-of-mockbuild/pytest-0/test_vhdr_versions__n_0/test.vhdr... Setting channel info structure... __________ test_brainvision_data_software_filters_latin1_global_units __________ mne/io/brainvision/tests/test_brainvision.py:476: in test_brainvision_data_software_filters_latin1_global_units with pytest.warns(RuntimeWarning, match="software filter"): E RuntimeWarning: No info on DataPoints found. Inferring number of samples from the data file size. ----------------------------- Captured stdout call ----------------------------- Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... 1 projection items deactivated Reading 0 ... 250 = 0.000 ... 1.000 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 250 = 0.000 ... 1.000 secs... Reading 0 ... 250 = 0.000 ... 1.000 secs... Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Reading 0 ... 250 = 0.000 ... 1.000 secs... Reading 0 ... 250 = 0.000 ... 1.000 secs... EEG channel type selected for re-referencing Applying average reference. Applying a custom ('EEG',) reference. EEG channel type selected for re-referencing Adding average EEG reference projection. 1 projection items deactivated Created an SSP operator (subspace dimension = 1) 1 projection items activated SSP projectors applied... Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Writing /tmp/tmp_mne_tempdir_o02pt1dt/test_raw.fif Closing /tmp/tmp_mne_tempdir_o02pt1dt/test_raw.fif [done] Opening raw data file /tmp/tmp_mne_tempdir_o02pt1dt/test_raw.fif... Isotrak not found Range : 0 ... 250 = 0.000 ... 1.000 secs Ready. Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... <RawBrainVision | test_old_layout_latin1_software_filter.eeg, 29 x 251 (1.0 s), ~85 kB, data loaded> Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... <RawBrainVision | test_old_layout_latin1_software_filter.eeg, 29 x 251 (1.0 s), ~28 kB, data not loaded> Extracting parameters from test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... _______________________ test_brainvision_vectorized_data _______________________ mne/io/brainvision/tests/test_brainvision.py:569: in test_brainvision_vectorized_data with pytest.warns(RuntimeWarning, match="software filter"): E RuntimeWarning: No info on DataPoints found. Inferring number of samples from the data file size. ----------------------------- Captured stdout call ----------------------------- Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/test_old_layout_latin1_software_filter.vhdr... Setting channel info structure... Reading 0 ... 250 = 0.000 ... 1.000 secs... __________________________ test_coodinates_extraction __________________________ mne/io/brainvision/tests/test_brainvision.py:614: in test_coodinates_extraction with pytest.warns(RuntimeWarning, match="coordinate information"): E RuntimeWarning: Not setting positions of 4 misc channels found in montage: E ['HL', 'HR', 'Vb', 'ReRef'] E Consider setting the channel types to be of EEG/sEEG/ECoG/DBS/fNIRS using inst.set_channel_types before calling inst.set_montage, or omit these channels when creating your montage. ----------------------------- Captured stdout call ----------------------------- Extracting parameters from /builddir/build/BUILD/mne-python-1.6.1/mne/io/brainvision/tests/data/testv2.vhdr... Setting channel info structure... ___________________________ test_ica_simple[fastica] ___________________________ mne/preprocessing/tests/test_ica.py:174: in test_ica_simple with pytest.warns(RuntimeWarning, match="No average EEG.*"): E RuntimeWarning: The data has not been high-pass filtered. For good ICA performance, it should be high-pass filtered (e.g., with a 1.0 Hz lower bound) before fitting ICA. ----------------------------- Captured stdout call ----------------------------- Creating RawArray with float64 data, n_channels=3, n_times=1000 Range : 0 ... 999 = 0.000 ... 0.999 secs Ready. Fitting ICA to data using 3 channels (please be patient, this may take a while) Computing rank from covariance with rank=None Using tolerance 2.7e-19 (2.2e-16 eps * 3 dim * 0.0004 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Setting small EEG eigenvalues to zero (without PCA) Created the whitener using a noise covariance matrix with rank 3 (0 small eigenvalues omitted) Selecting by number: 3 components Fitting ICA took 0.0s. ______________________ test_get_explained_variance_ratio _______________________ mne/preprocessing/tests/test_ica.py:1035: in test_get_explained_variance_ratio with pytest.warns(RuntimeWarning, match="were baseline-corrected"): E sklearn.exceptions.ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. ---------------------------- Captured stdout setup ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 3003 = 0.000 ... 5.000 secs... Not setting metadata 5 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) Using data from preloaded Raw for 5 events and 41 original time points ... 2 bad epochs dropped Not setting metadata 3 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 3 events and 41 original time points ... 0 bad epochs dropped ----------------------------- Captured stdout call ----------------------------- Fitting ICA to data using 38 channels (please be patient, this may take a while) Selecting by non-zero PCA components: 38 components Fitting ICA took 0.0s. ____________________ test_fit_params_epochs_vs_raw[start-0] ____________________ mne/preprocessing/tests/test_ica.py:1258: in test_fit_params_epochs_vs_raw with pytest.warns(RuntimeWarning, match="parameters.*will be ignored"): E RuntimeWarning: The epochs you passed to ICA.fit() were baseline-corrected. However, we suggest to fit ICA only on data that has been high-pass filtered, but NOT baseline-corrected. ----------------------------- Captured stdout call ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Removing projector <Projection | PCA-v1, active : False, n_channels : 102> Removing projector <Projection | PCA-v2, active : False, n_channels : 102> Removing projector <Projection | PCA-v3, active : False, n_channels : 102> Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Fitting ICA to data using 60 channels (please be patient, this may take a while) Loading data for 31 events and 421 original time points ... 1 bad epochs dropped Selecting by number: 3 components Loading data for 30 events and 421 original time points ... Fitting ICA took 0.1s. ___________________ test_fit_params_epochs_vs_raw[stop-500] ____________________ mne/preprocessing/tests/test_ica.py:1258: in test_fit_params_epochs_vs_raw with pytest.warns(RuntimeWarning, match="parameters.*will be ignored"): E RuntimeWarning: The epochs you passed to ICA.fit() were baseline-corrected. However, we suggest to fit ICA only on data that has been high-pass filtered, but NOT baseline-corrected. ----------------------------- Captured stdout call ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Removing projector <Projection | PCA-v1, active : False, n_channels : 102> Removing projector <Projection | PCA-v2, active : False, n_channels : 102> Removing projector <Projection | PCA-v3, active : False, n_channels : 102> Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Fitting ICA to data using 60 channels (please be patient, this may take a while) Loading data for 31 events and 421 original time points ... 1 bad epochs dropped Selecting by number: 3 components Loading data for 30 events and 421 original time points ... Fitting ICA took 0.1s. _______________ test_fit_params_epochs_vs_raw[reject-param_val2] _______________ mne/preprocessing/tests/test_ica.py:1258: in test_fit_params_epochs_vs_raw with pytest.warns(RuntimeWarning, match="parameters.*will be ignored"): E RuntimeWarning: The epochs you passed to ICA.fit() were baseline-corrected. However, we suggest to fit ICA only on data that has been high-pass filtered, but NOT baseline-corrected. ----------------------------- Captured stdout call ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Removing projector <Projection | PCA-v1, active : False, n_channels : 102> Removing projector <Projection | PCA-v2, active : False, n_channels : 102> Removing projector <Projection | PCA-v3, active : False, n_channels : 102> Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Fitting ICA to data using 60 channels (please be patient, this may take a while) Loading data for 31 events and 421 original time points ... 1 bad epochs dropped Selecting by number: 3 components Loading data for 30 events and 421 original time points ... Fitting ICA took 0.1s. ________________ test_fit_params_epochs_vs_raw[flat-param_val3] ________________ mne/preprocessing/tests/test_ica.py:1258: in test_fit_params_epochs_vs_raw with pytest.warns(RuntimeWarning, match="parameters.*will be ignored"): E RuntimeWarning: The epochs you passed to ICA.fit() were baseline-corrected. However, we suggest to fit ICA only on data that has been high-pass filtered, but NOT baseline-corrected. ----------------------------- Captured stdout call ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Removing projector <Projection | PCA-v1, active : False, n_channels : 102> Removing projector <Projection | PCA-v2, active : False, n_channels : 102> Removing projector <Projection | PCA-v3, active : False, n_channels : 102> Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Fitting ICA to data using 60 channels (please be patient, this may take a while) Loading data for 31 events and 421 original time points ... 1 bad epochs dropped Selecting by number: 3 components Loading data for 30 events and 421 original time points ... Fitting ICA took 0.1s. _________________________ test_compute_proj_ecg[True] __________________________ mne/preprocessing/tests/test_ssp.py:72: in test_compute_proj_ecg with pytest.warns(RuntimeWarning, match="No good epochs found"): E RuntimeWarning: filter_length (8192) is longer than the signal (4205), distortion is likely. Reduce filter length or filter a longer signal. ---------------------------- Captured stdout setup ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 4204 = 0.000 ... 6.999 secs... ----------------------------- Captured stdout call ----------------------------- Adding average EEG reference projection. Running ECG SSP computation Using channel MEG 1531 to identify heart beats. Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 1000 samples (1.665 s) Number of ECG events detected : 6 (average pulse 51 / min.) Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 1000 samples (1.665 s) Not setting metadata 6 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 6 events and 3124 original time points ... 4 bad epochs dropped Adding projection: planar--0.200-5.000-PCA-01 Adding projection: planar--0.200-5.000-PCA-02 Adding projection: axial--0.200-5.000-PCA-01 Adding projection: axial--0.200-5.000-PCA-02 Adding projection: eeg--0.200-5.000-PCA-01 Adding projection: eeg--0.200-5.000-PCA-02 Done. Adding average EEG reference projection. Running ECG SSP computation Using channel MEG 1531 to identify heart beats. Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 6007 samples (10.001 s) Number of ECG events detected : 14 (average pulse 119 / min.) Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 6007 samples (10.001 s) Not setting metadata 14 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 14 events and 3124 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 14 bad epochs dropped ----------------------------- Captured stderr call ----------------------------- [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s _________________________ test_compute_proj_ecg[False] _________________________ mne/preprocessing/tests/test_ssp.py:72: in test_compute_proj_ecg with pytest.warns(RuntimeWarning, match="No good epochs found"): E RuntimeWarning: filter_length (8192) is longer than the signal (4205), distortion is likely. Reduce filter length or filter a longer signal. ---------------------------- Captured stdout setup ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 4204 = 0.000 ... 6.999 secs... ----------------------------- Captured stdout call ----------------------------- Adding average EEG reference projection. Running ECG SSP computation Using channel MEG 1531 to identify heart beats. Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 1000 samples (1.665 s) Number of ECG events detected : 6 (average pulse 51 / min.) Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 1000 samples (1.665 s) Not setting metadata 6 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 6 events and 3124 original time points ... 4 bad epochs dropped Adding projection: planar-999--0.200-5.000-PCA-01 Adding projection: planar-999--0.200-5.000-PCA-02 Adding projection: axial-999--0.200-5.000-PCA-01 Adding projection: axial-999--0.200-5.000-PCA-02 Adding projection: eeg-999--0.200-5.000-PCA-01 Adding projection: eeg-999--0.200-5.000-PCA-02 Done. Adding average EEG reference projection. Running ECG SSP computation Using channel MEG 1531 to identify heart beats. Setting up band-pass filter from 5 - 35 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 5.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 4.75 Hz) - Upper passband edge: 35.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 35.25 Hz) - Filter length: 6007 samples (10.001 s) Number of ECG events detected : 14 (average pulse 119 / min.) Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 6007 samples (10.001 s) Not setting metadata 14 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 14 events and 3124 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 14 bad epochs dropped ----------------------------- Captured stderr call ----------------------------- [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.0s _________________________ test_compute_proj_eog[True] __________________________ mne/preprocessing/tests/test_ssp.py:133: in test_compute_proj_eog with pytest.warns(RuntimeWarning, match="longer"): E RuntimeWarning: All epochs were dropped! E You might need to alter reject/flat-criteria or drop bad channels to avoid this. You can use Epochs.plot_drop_log() to see which channels are responsible for the dropping of epochs. ---------------------------- Captured stdout setup ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 4204 = 0.000 ... 6.999 secs... ----------------------------- Captured stdout call ----------------------------- Including 3 SSP projectors from raw file Adding average EEG reference projection. Running EOG SSP computation Using EOG channel: EOG 061 EOG channel index for this subject is: [91] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (1.665 s) Now detecting blinks and generating corresponding events Found 3 significant peaks Number of EOG events detected: 3 Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 1000 samples (1.665 s) Not setting metadata 3 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 3 events and 3124 original time points ... 2 bad epochs dropped Adding projection: planar--0.200-5.000-PCA-01 Adding projection: planar--0.200-5.000-PCA-02 Adding projection: axial--0.200-5.000-PCA-01 Adding projection: axial--0.200-5.000-PCA-02 Adding projection: eeg--0.200-5.000-PCA-01 Adding projection: eeg--0.200-5.000-PCA-02 Done. Including 3 SSP projectors from raw file Adding average EEG reference projection. Running EOG SSP computation Using EOG channel: EOG 061 EOG channel index for this subject is: [91] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 6007 samples (10.001 s) Now detecting blinks and generating corresponding events Found 4 significant peaks Number of EOG events detected: 4 Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 6007 samples (10.001 s) Not setting metadata 4 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 4 events and 3124 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped ----------------------------- Captured stderr call ----------------------------- [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.0s _________________________ test_compute_proj_eog[False] _________________________ mne/preprocessing/tests/test_ssp.py:133: in test_compute_proj_eog with pytest.warns(RuntimeWarning, match="longer"): E RuntimeWarning: All epochs were dropped! E You might need to alter reject/flat-criteria or drop bad channels to avoid this. You can use Epochs.plot_drop_log() to see which channels are responsible for the dropping of epochs. ---------------------------- Captured stdout setup ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 4204 = 0.000 ... 6.999 secs... ----------------------------- Captured stdout call ----------------------------- Including 3 SSP projectors from raw file Adding average EEG reference projection. Running EOG SSP computation Using EOG channel: EOG 061 EOG channel index for this subject is: [91] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 1000 samples (1.665 s) Now detecting blinks and generating corresponding events Found 3 significant peaks Number of EOG events detected: 3 Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 1000 samples (1.665 s) Not setting metadata 3 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 3 events and 3124 original time points ... 2 bad epochs dropped Adding projection: planar-998--0.200-5.000-PCA-01 Adding projection: planar-998--0.200-5.000-PCA-02 Adding projection: axial-998--0.200-5.000-PCA-01 Adding projection: axial-998--0.200-5.000-PCA-02 Adding projection: eeg-998--0.200-5.000-PCA-01 Adding projection: eeg-998--0.200-5.000-PCA-02 Done. Including 3 SSP projectors from raw file Adding average EEG reference projection. Running EOG SSP computation Using EOG channel: EOG 061 EOG channel index for this subject is: [91] Filtering the data to remove DC offset to help distinguish blinks from saccades Selecting channel EOG 061 for blink detection Setting up band-pass filter from 1 - 10 Hz FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter: - Windowed frequency-domain design (firwin2) method - Hann window - Lower passband edge: 1.00 - Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz) - Upper passband edge: 10.00 Hz - Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz) - Filter length: 6007 samples (10.001 s) Now detecting blinks and generating corresponding events Found 4 significant peaks Number of EOG events detected: 4 Computing projector Filtering raw data in 1 contiguous segment FIR filter parameters --------------------- Designing a two-pass forward and reverse, zero-phase, non-causal allpass filter: - Windowed frequency-domain design (firwin2) method - Hamming window - Filter length: 6007 samples (10.001 s) Not setting metadata 4 matching events found No baseline correction applied Created an SSP operator (subspace dimension = 3) 3 projection items activated Using data from preloaded Raw for 4 events and 3124 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] 4 bad epochs dropped ----------------------------- Captured stderr call ----------------------------- [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s [Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.1s ____________________________ test_cache_dir[NumPy] _____________________________ mne/stats/tests/test_cluster_level.py:143: in test_cache_dir with pytest.warns(RuntimeWarning, match="independently"): E RuntimeWarning: Automatic threshold is only valid for stat_fun=None (or ttest_1samp_no_p), got functools.partial(<function ttest_1samp_no_p at 0x7f32ef440040>, sigma=0.001) ________________ test_cluster_permutation_with_adjacency[NumPy] ________________ mne/stats/tests/test_cluster_level.py:513: in test_cluster_permutation_with_adjacency spatio_temporal_func( <decorator-gen-285>:12: in spatio_temporal_cluster_1samp_test ??? mne/stats/cluster_level.py:1454: in spatio_temporal_cluster_1samp_test return permutation_cluster_1samp_test( <decorator-gen-284>:12: in permutation_cluster_1samp_test ??? mne/stats/cluster_level.py:1357: in permutation_cluster_1samp_test return _permutation_cluster_test( mne/stats/cluster_level.py:977: in _permutation_cluster_test raise ValueError( E ValueError: t_obs.shape (99,) provided by stat_fun <function test_cluster_permutation_with_adjacency.<locals>.<lambda> at 0x7f32ba533e20> is not compatible with the sample shape (2, 50) During handling of the above exception, another exception occurred: E Failed: DID NOT WARN. No warnings of type (<class 'RuntimeWarning'>,) matching the regex were emitted. Regex: No clusters Emitted warnings: [RuntimeWarning('invalid value encountered in divide')]. All traceback entries are hidden. Pass `--full-trace` to see hidden and internal frames. During handling of the above exception, another exception occurred: mne/stats/tests/test_cluster_level.py:512: in test_cluster_permutation_with_adjacency with pytest.warns(RuntimeWarning, match="No clusters"): E RuntimeWarning: invalid value encountered in divide ----------------------------- Captured stdout call ----------------------------- stat_fun(H1): min=-1.653153 max=8.712598 Running initial clustering … Found 2 clusters stat_fun(H1): min=-1.653153 max=8.712598 Running initial clustering … Found 2 clusters stat_fun(H1): min=-1.653153 max=8.712598 Running initial clustering … Found 4 clusters stat_fun(H1): min=-1.653153 max=8.712598 No disjoint adjacency sets found Running initial clustering … Found 4 clusters stat_fun(H1): min=-1.653153 max=8.712598 Running initial clustering … Found 2 clusters stat_fun(H1): min=-1.653153 max=8.712598 Running initial clustering … Found 2 clusters stat_fun(H1): min=-1.653153 max=8.712598 Running initial clustering … Found 2 clusters stat_fun(H1): min=-1.653153 max=8.712598 Running initial clustering … stat_fun(H1): min=-1.653153 max=8.712598 Running initial clustering … Found 100 clusters stat_fun(H1): min=-1.653153 max=8.712598 Running initial clustering … stat_fun(H1): min=-1.653153 max=8.712598 Running initial clustering … Found 100 clusters Using a threshold of 1.729133 stat_fun(H1): min=-1.653153 max=8.712598 Running initial clustering … Using 8 thresholds from 1.00 to 8.00 for TFCE computation (h_power=2.00, e_power=0.50) Found 100 clusters stat_fun(H1): min=nan max=nan ----------------------------- Captured stderr call ----------------------------- 100%|██████████| Permuting : 49/49 [00:00<00:00, 3294.29it/s] 100%|██████████| Permuting : 49/49 [00:00<00:00, 1609.87it/s] 100%|██████████| Permuting : 49/49 [00:00<00:00, 1176.34it/s] 100%|██████████| Permuting : 49/49 [00:00<00:00, 849.54it/s] 100%|██████████| Permuting : 49/49 [00:00<00:00, 808.31it/s] 100%|██████████| Permuting : 49/49 [00:00<00:00, 1012.34it/s] 100%|██████████| Permuting : 1023/1023 [00:00<00:00, 3000.80it/s] 100%|██████████| Permuting : 1023/1023 [00:00<00:00, 3184.42it/s] 100%|██████████| Permuting : 49/49 [00:00<00:00, 286.83it/s] _________________________ test_fit_sphere_to_headshape _________________________ mne/tests/test_bem.py:449: in test_fit_sphere_to_headshape with pytest.warns(RuntimeWarning, match="Estimated head radius"): E RuntimeWarning: Only 4 head digitization points of the specified kinds ("cardinal", "extra",), fitting may be inaccurate ----------------------------- Captured stdout call ----------------------------- Fitted sphere radius: 90.0 mm Origin head coordinates: 0.5 -10.0 40.0 mm Origin device coordinates: 0.5 -5.0 50.0 mm Fitted sphere radius: 90.0 mm Origin head coordinates: 0.5 -9.9 39.9 mm Origin device coordinates: 0.5 -4.9 49.9 mm Fitted sphere radius: 89.9 mm Origin head coordinates: 0.5 -9.9 40.0 mm Origin device coordinates: 0.5 -4.9 50.0 mm Fitted sphere radius: 120.0 mm Origin head coordinates: 0.5 -10.0 40.0 mm Origin device coordinates: 0.5 -5.0 50.0 mm __________________________________ test_crop ___________________________________ mne/tests/test_epochs.py:2274: in test_crop with pytest.warns(RuntimeWarning, match="tmax is set to"): E RuntimeWarning: tmin is not in time interval. tmin is set to <class 'mne.epochs.Epochs'>.tmin (-0.199795 s) ----------------------------- Captured stdout call ----------------------------- Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped Not setting metadata 1 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 1 events and 421 original time points ... 0 bad epochs dropped ___________________________ test_concatenate_epochs ____________________________ mne/tests/test_epochs.py:3593: in test_concatenate_epochs with pytest.warns(RuntimeWarning, match="was empty"): E RuntimeWarning: epochs._get_data() can't run because this Epochs-object is empty. You might want to check Epochs.drop_log or Epochs.plot_drop_log() to see why epochs were dropped. ----------------------------- Captured stdout call ----------------------------- Not setting metadata 7 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Loading data for 7 events and 421 original time points ... 0 bad epochs dropped Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Applying baseline correction (mode: mean) Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... Loading data for 7 events and 421 original time points ... Not setting metadata 14 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 421 original time points ... Loading data for 0 events and 421 original time points ... Not setting metadata 7 matching events found Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) ________________________________ test_no_epochs ________________________________ mne/tests/test_epochs.py:4204: in test_no_epochs with pytest.warns(RuntimeWarning, match="no data"): E RuntimeWarning: All epochs were dropped! E You might need to alter reject/flat-criteria or drop bad channels to avoid this. You can use Epochs.plot_drop_log() to see which channels are responsible for the dropping of epochs. ----------------------------- Captured stdout call ----------------------------- Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Loading data for 31 events and 421 original time points ... Rejecting epoch based on EOG : ['EOG 061'] Rejecting epoch based on EOG : ['EOG 061'] Rejecting epoch based on EOG : ['EOG 061'] 4 bad epochs dropped Loading data for 1 events and 421 original time points ... Loading data for 27 events and 421 original time points ... Not setting metadata 31 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) 3 projection items activated Overwriting existing file. Loading data for 31 events and 421 original time points ... Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] Rejecting epoch based on GRAD : ['MEG 2443'] 31 bad epochs dropped Overwriting existing file. Loading data for 0 events and 421 original time points ... _________________________ test_time_as_index_and_crop __________________________ mne/tests/test_evoked.py:802: in test_time_as_index_and_crop with pytest.warns(RuntimeWarning, match="tmax is set to"): E RuntimeWarning: tmin is not in time interval. tmin is set to <class 'mne.evoked.Evoked'>.tmin (-0.0998976 s) ----------------------------- Captured stdout call ----------------------------- Reading /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied ________________________ test_how_to_deal_with_warnings ________________________ mne/utils/tests/test_logging.py:66: in test_how_to_deal_with_warnings with pytest.warns(UserWarning, match="bb") as w: E UserWarning: aa warning ______________________ test_plot_epochs_basic[matplotlib] ______________________ mne/viz/tests/test_epochs.py:55: in test_plot_epochs_basic with pytest.warns(RuntimeWarning, match="projection"): E RuntimeWarning: Mean of empty slice ---------------------------- Captured stdout setup ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 1 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 1 events and 662 original time points ... 0 bad epochs dropped Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Not setting metadata 7 matching events found Setting baseline interval to [-0.09989760657919393, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 3) Loading data for 7 events and 662 original time points ... 0 bad epochs dropped 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active ----------------------------- Captured stdout call ----------------------------- Computing rank from covariance with rank=None Using tolerance 2.1e-15 (2.2e-16 eps * 13 dim * 0.71 max singular value) Estimated rank (grad): 13 GRAD: rank 13 computed from 13 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 4.8e-17 (2.2e-16 eps * 6 dim * 0.036 max singular value) Estimated rank (mag): 3 MAG: rank 3 computed from 6 data channels with 3 projectors Computing rank from covariance with rank=None Using tolerance 1.9e-15 (2.2e-16 eps * 12 dim * 0.7 max singular value) Estimated rank (grad): 12 GRAD: rank 12 computed from 12 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 4.8e-17 (2.2e-16 eps * 6 dim * 0.036 max singular value) Estimated rank (mag): 3 MAG: rank 3 computed from 6 data channels with 3 projectors Computing rank from covariance with rank=None Using tolerance 1.7e-15 (2.2e-16 eps * 11 dim * 0.7 max singular value) Estimated rank (grad): 11 GRAD: rank 11 computed from 11 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 4.8e-17 (2.2e-16 eps * 6 dim * 0.036 max singular value) Estimated rank (mag): 3 MAG: rank 3 computed from 6 data channels with 3 projectors _____________________________ test_plot_evoked_cov _____________________________ mne/viz/tests/test_evoked.py:122: in test_plot_evoked_cov with pytest.warns(RuntimeWarning, match="relative scaling"): E RuntimeWarning: No average EEG reference present in info["projs"], covariance may be adversely affected. Consider recomputing covariance using with an average eeg reference projector added. ----------------------------- Captured stdout call ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated 366 x 366 full covariance (kind = 1) found. Read a total of 4 projection items: PCA-v1 (1 x 102) active PCA-v2 (1 x 102) active PCA-v3 (1 x 102) active Average EEG reference (1 x 60) active Computing rank from covariance with rank=None Using tolerance 6.6e-16 (2.2e-16 eps * 3 dim * 1 max singular value) Estimated rank (eeg): 3 EEG: rank 3 computed from 3 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.3e-15 (2.2e-16 eps * 9 dim * 0.64 max singular value) Estimated rank (grad): 9 GRAD: rank 9 computed from 9 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 3.7e-17 (2.2e-16 eps * 2 dim * 0.083 max singular value) Estimated rank (mag): 2 MAG: rank 2 computed from 2 data channels with 0 projectors Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_chpi_raw_sss.fif... Range : 116000 ... 121000 = 116.000 ... 121.000 secs Ready. Not setting metadata 5 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Loading data for 5 events and 701 original time points ... 1 bad epochs dropped Computing rank from data with rank=None Using tolerance 3.2e-09 (2.2e-16 eps * 12 dim * 1.2e+06 max singular value) Estimated rank (mag + grad): 12 Found multiple SSS records. Using the first. MEG: rank 12 computed from 12 data channels with 0 projectors Using tolerance 6e-12 (2.2e-16 eps * 4 dim * 6.8e+03 max singular value) Estimated rank (eeg): 4 EEG: rank 4 computed from 4 data channels with 0 projectors Reducing data rank from 16 -> 16 Estimating covariance using EMPIRICAL Done. Number of samples used : 2804 [done] Computing rank from covariance with rank=None Using tolerance 1.5e-13 (2.2e-16 eps * 4 dim * 1.6e+02 max singular value) Estimated rank (eeg): 4 EEG: rank 4 computed from 4 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.2e-10 (2.2e-16 eps * 12 dim * 4.4e+04 max singular value) Estimated rank (mag + grad): 12 Found multiple SSS records. Using the first. MEG: rank 12 computed from 12 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.2e-10 (2.2e-16 eps * 12 dim * 4.4e+04 max singular value) Estimated rank (mag + grad): 12 Found multiple SSS records. Using the first. MEG: rank 12 computed from 12 data channels with 0 projectors Computing rank from covariance with rank=None Using tolerance 1.5e-13 (2.2e-16 eps * 4 dim * 1.6e+02 max singular value) Estimated rank (eeg): 4 EEG: rank 4 computed from 4 data channels with 0 projectors ____________________________ test_plot_evoked_image ____________________________ mne/viz/tests/test_evoked.py:336: in test_plot_evoked_image with pytest.warns(RuntimeWarning, match="not adding contour"): E RuntimeWarning: `mask` is None, not masking the plot ... ----------------------------- Captured stdout call ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated No projector specified for this dataset. Please consider the method self.add_proj. _______________________________ test_plot_events _______________________________ mne/viz/tests/test_misc.py:217: in test_plot_events with pytest.warns(RuntimeWarning, match=r"vent \d+ missing from event_id"): E RuntimeWarning: Color was not assigned for events 5, 32. Default colors will be used. ----------------------------- Captured stdout call ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Reading 0 ... 14399 = 0.000 ... 23.974 secs... ______________________________ test_plot_tfr_topo ______________________________ mne/viz/tests/test_topo.py:328: in test_plot_tfr_topo with pytest.warns(RuntimeWarning, match="not masking"): E RuntimeWarning: `mask` is None, not adding contour to the plot ... ----------------------------- Captured stdout call ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-1.6.1/mne/io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. 0 projection items deactivated Not setting metadata 2 matching events found Setting baseline interval to [-0.19979521315838786, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Applying baseline correction (mode: ratio) Applying baseline correction (mode: ratio) ================================== XFAILURES =================================== ____________________ test_permutation_t_test_tail[less--1] _____________________ mne/stats/tests/test_permutations.py:80: in test_permutation_t_test_tail assert_allclose(p_values[0], p_values_scipy, rtol=1e-2) /usr/lib64/python3.12/contextlib.py:81: in inner return func(*args, **kwds) E AssertionError: E Not equal to tolerance rtol=0.01, atol=0 E E Mismatched elements: 1 / 1 (100%) E Max absolute difference: 0.41856325 E Max relative difference: 0.99838813 E x: array(0.837802) E y: array(0.419239) ----------------------------- Captured stdout call ----------------------------- Permuting 262142 times... ___________________ test_permutation_t_test_tail[greater-1] ____________________ mne/stats/tests/test_permutations.py:80: in test_permutation_t_test_tail assert_allclose(p_values[0], p_values_scipy, rtol=1e-2) /usr/lib64/python3.12/contextlib.py:81: in inner return func(*args, **kwds) E AssertionError: E Not equal to tolerance rtol=0.01, atol=0 E E Mismatched elements: 1 / 1 (100%) E Max absolute difference: 0.05414948 E Max relative difference: 0.05724951 E x: array(1.) E y: array(0.945851) ----------------------------- Captured stdout call ----------------------------- Permuting 262142 times... __ test_bids_splits_fail_for_bad_fname_ending[test-epo.fif-epochs_to_split1] ___ mne/tests/test_epochs.py:1694: in test_bids_splits_fail_for_bad_fname_ending with pytest.raises(ValueError, match=".* must end with an underscore"): E Failed: DID NOT RAISE <class 'ValueError'> ---------------------------- Captured stdout setup ----------------------------- Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped ----------------------------- Captured stdout call ----------------------------- Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 18 events and 701 original time points ... __ test_bids_splits_fail_for_bad_fname_ending[a_b_c-epo.fif-epochs_to_split0] __ mne/tests/test_epochs.py:1694: in test_bids_splits_fail_for_bad_fname_ending with pytest.raises(ValueError, match=".* must end with an underscore"): E Failed: DID NOT RAISE <class 'ValueError'> ---------------------------- Captured stdout setup ----------------------------- Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped ----------------------------- Captured stdout call ----------------------------- Using data from preloaded Raw for 1 events and 701 original time points ... Splitting into 3 parts Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... Using data from preloaded Raw for 6 events and 701 original time points ... __ test_bids_splits_fail_for_bad_fname_ending[a_b_c-epo.fif-epochs_to_split1] __ mne/tests/test_epochs.py:1694: in test_bids_splits_fail_for_bad_fname_ending with pytest.raises(ValueError, match=".* must end with an underscore"): E Failed: DID NOT RAISE <class 'ValueError'> ---------------------------- Captured stdout setup ----------------------------- Creating RawArray with float64 data, n_channels=100, n_times=19000 Range : 0 ... 18999 = 0.000 ... 18.999 secs Ready. Not setting metadata 19 matching events found Setting baseline interval to [-0.2, 0.0] s Applying baseline correction (mode: mean) 0 projection items activated Using data from preloaded Raw for 19 events and 701 original time points ... 1 bad epochs dropped ----------------------------- Captured stdout call ----------------------------- Using data from preloaded Raw for 1 events and 701 original time points ... Using data from preloaded Raw for 18 events and 701 original time points ... _____________________ test_backend_environment_setup[foo] ______________________ mne/viz/backends/tests/test_renderer.py:40: in test_backend_environment_setup renderer.set_3d_backend(backend) <decorator-gen-332>:12: in set_3d_backend ??? mne/viz/backends/renderer.py:141: in set_3d_backend backend_name = _check_3d_backend_name(backend_name) mne/viz/backends/renderer.py:63: in _check_3d_backend_name _check_option("backend_name", backend_name, VALID_3D_BACKENDS) mne/utils/check.py:880: in _check_option raise ValueError( E ValueError: Invalid value for the 'backend_name' parameter. Allowed values are 'pyvistaqt' and 'notebook', but got 'foo' instead. FAILED mne/commands/tests/test_commands.py::test_maxfilter - FutureWarning: NOTE: apply_maxfilter() is a deprecated function. apply_maxf... FAILED mne/forward/tests/test_make_forward.py::test_magnetic_dipole - RuntimeWarning: invalid value encountered in divide FAILED mne/io/brainvision/tests/test_brainvision.py::test_vhdr_versions[\n] - RuntimeWarning: MNE-Python currently only supports header versions 1.0 ... FAILED mne/io/brainvision/tests/test_brainvision.py::test_brainvision_data_software_filters_latin1_global_units - RuntimeWarning: No info on DataPoints found. Inferring number of samples fr... FAILED mne/io/brainvision/tests/test_brainvision.py::test_brainvision_vectorized_data - RuntimeWarning: No info on DataPoints found. Inferring number of samples fr... FAILED mne/io/brainvision/tests/test_brainvision.py::test_coodinates_extraction - RuntimeWarning: Not setting positions of 4 misc channels found in montage: FAILED mne/preprocessing/tests/test_ica.py::test_ica_simple[fastica] - RuntimeWarning: The data has not been high-pass filtered. For good ICA perf... FAILED mne/preprocessing/tests/test_ica.py::test_get_explained_variance_ratio - sklearn.exceptions.ConvergenceWarning: FastICA did not converge. Consider i... FAILED mne/preprocessing/tests/test_ica.py::test_fit_params_epochs_vs_raw[start-0] - RuntimeWarning: The epochs you passed to ICA.fit() were baseline-corrected.... FAILED mne/preprocessing/tests/test_ica.py::test_fit_params_epochs_vs_raw[stop-500] - RuntimeWarning: The epochs you passed to ICA.fit() were baseline-corrected.... FAILED mne/preprocessing/tests/test_ica.py::test_fit_params_epochs_vs_raw[reject-param_val2] - RuntimeWarning: The epochs you passed to ICA.fit() were baseline-corrected.... FAILED mne/preprocessing/tests/test_ica.py::test_fit_params_epochs_vs_raw[flat-param_val3] - RuntimeWarning: The epochs you passed to ICA.fit() were baseline-corrected.... FAILED mne/preprocessing/tests/test_ssp.py::test_compute_proj_ecg[True] - RuntimeWarning: filter_length (8192) is longer than the signal (4205), dist... FAILED mne/preprocessing/tests/test_ssp.py::test_compute_proj_ecg[False] - RuntimeWarning: filter_length (8192) is longer than the signal (4205), dist... FAILED mne/preprocessing/tests/test_ssp.py::test_compute_proj_eog[True] - RuntimeWarning: All epochs were dropped! FAILED mne/preprocessing/tests/test_ssp.py::test_compute_proj_eog[False] - RuntimeWarning: All epochs were dropped! FAILED mne/stats/tests/test_cluster_level.py::test_cache_dir[NumPy] - RuntimeWarning: Automatic threshold is only valid for stat_fun=None (or tte... FAILED mne/stats/tests/test_cluster_level.py::test_cluster_permutation_with_adjacency[NumPy] - RuntimeWarning: invalid value encountered in divide FAILED mne/tests/test_bem.py::test_fit_sphere_to_headshape - RuntimeWarning: Only 4 head digitization points of the specified kinds ("ca... FAILED mne/tests/test_epochs.py::test_crop - RuntimeWarning: tmin is not in time interval. tmin is set to <class 'mne.ep... FAILED mne/tests/test_epochs.py::test_concatenate_epochs - RuntimeWarning: epochs._get_data() can't run because this Epochs-object is ... FAILED mne/tests/test_epochs.py::test_no_epochs - RuntimeWarning: All epochs were dropped! FAILED mne/tests/test_evoked.py::test_time_as_index_and_crop - RuntimeWarning: tmin is not in time interval. tmin is set to <class 'mne.ev... FAILED mne/utils/tests/test_logging.py::test_how_to_deal_with_warnings - UserWarning: aa warning FAILED mne/viz/tests/test_epochs.py::test_plot_epochs_basic[matplotlib] - RuntimeWarning: Mean of empty slice FAILED mne/viz/tests/test_evoked.py::test_plot_evoked_cov - RuntimeWarning: No average EEG reference present in info["projs"], covarian... FAILED mne/viz/tests/test_evoked.py::test_plot_evoked_image - RuntimeWarning: `mask` is None, not masking the plot ... FAILED mne/viz/tests/test_misc.py::test_plot_events - RuntimeWarning: Color was not assigned for events 5, 32. Default colors wil... FAILED mne/viz/tests/test_topo.py::test_plot_tfr_topo - RuntimeWarning: `mask` is None, not adding contour to the plot ... = 29 failed, 2628 passed, 1031 skipped, 483 deselected, 6 xfailed, 7 warnings in 319.79s (0:05:19) = https://docs.pytest.org/en/stable/changelog.html For the build logs, see: https://copr-be.cloud.fedoraproject.org/results/thrnciar/pytest/fedora-rawhide-x86_64/07248386-python-mne/ For all our attempts to build python-mne with pytest 8, see: https://copr.fedorainfracloud.org/coprs/thrnciar/pytest/package/python-mne/ Let us know here if you have any questions. Pytest 8 is planned to be included in Fedora 41. And this bugzilla is a heads up before we merge new pytest into rawhide. For more info see a Fedora Change proposal https://fedoraproject.org/wiki/Changes/Pytest_8 We'd appreciate help from the people who know this package best, but if you don't want to work on this now, let us know so we can try to work around it on our side.
PR: https://src.fedoraproject.org/rpms/python-mne/pull-request/18