MRG, MAINT: Lower memory again

.circleci/config.yml

@@ -4,7 +4,7 @@ version: 2
 jobs:
     build_docs:
       docker:
-        - image: circleci/python:3.8.1-buster
+        - image: circleci/python:3.8.5-buster
       steps:
         - checkout
         - run:

.gitignore

@@ -55,6 +55,7 @@ doc/auto_examples/
 doc/auto_tutorials/
 doc/modules/generated/
 doc/sphinxext/cachedir
+tutorials/misc/report.h5
 pip-log.txt
 .coverage*
 tags

doc/carousel.inc

@@ -14,7 +14,7 @@
         <div class="carousel-inner">
 
           <div class="item active">
-            <div class="chopper container" style="background-image: url(_images/sphx_glr_plot_mne_dspm_source_localization_009.png)" title="dSPM">
+            <div class="chopper container" style="background-image: url(_images/sphx_glr_plot_mne_dspm_source_localization_008.png)" title="dSPM">
               <div class="carousel-caption">
                 <h3>Source estimation</h3>
                 <p>Distributed, sparse, mixed-norm, beamformers, dipole fitting, and more.

examples/forward/plot_left_cerebellum_volume_source.py

@@ -50,21 +50,18 @@
 mne.viz.set_3d_view(fig, azimuth=173.78, elevation=101.75,
                     distance=0.30, focalpoint=(-0.03, -0.01, 0.03))
 
-##############################################################################
-# Compare volume source locations to segmentation file in freeview
 
-# Export source positions to nift file
-nii_fname = data_path + '/MEG/sample/mne_sample_lh-cerebellum-cortex.nii'
-
-src.export_volume(nii_fname, mri_resolution=True)
-
-# Uncomment the following lines to display source positions in freeview.
-'''
-# display image in freeview
-from mne.utils import run_subprocess
-mri_fname = subjects_dir + '/sample/mri/brain.mgz'
-run_subprocess(['freeview', '-v', mri_fname, '-v',
-                '%s:colormap=lut:opacity=0.5' % aseg_fname, '-v',
-                '%s:colormap=jet:colorscale=0,2' % nii_fname, '-slice',
-                '157 75 105'])
-'''
+###############################################################################
+# You can export source positions to a NIfTI file::
+#
+#     >>> nii_fname = 'mne_sample_lh-cerebellum-cortex.nii'
+#     >>> src.export_volume(nii_fname, mri_resolution=True)
+#
+# And display source positions in freeview::
+#
+#    >>> from mne.utils import run_subprocess
+#    >>> mri_fname = subjects_dir + '/sample/mri/brain.mgz'
+#    >>> run_subprocess(['freeview', '-v', mri_fname, '-v',
+#                        '%s:colormap=lut:opacity=0.5' % aseg_fname, '-v',
+#                        '%s:colormap=jet:colorscale=0,2' % nii_fname,
+#                        '-slice', '157 75 105'])

examples/inverse/plot_covariance_whitening_dspm.py

@@ -1,4 +1,7 @@
 """
+
+.. _ex-covariance-whitening-dspm:
+
 ===================================================
 Demonstrate impact of whitening on source estimates
 ===================================================

examples/inverse/plot_custom_inverse_solver.py

@@ -36,7 +36,7 @@
 noise_cov = mne.read_cov(cov_fname)
 # Handling average file
 evoked = mne.read_evokeds(ave_fname, condition=condition, baseline=(None, 0))
-evoked.crop(tmin=0.04, tmax=0.18, verbose='error')  # ignore baseline
+evoked.crop(tmin=0.04, tmax=0.18)
 
 evoked = evoked.pick_types(eeg=False, meg=True)
 # Handling forward solution

examples/inverse/plot_mixed_norm_inverse.py

@@ -36,7 +36,7 @@
 # Handling average file
 condition = 'Left Auditory'
 evoked = mne.read_evokeds(ave_fname, condition=condition, baseline=(None, 0))
-evoked.crop(tmin=0, tmax=0.3, verbose='error')  # ignore baseline
+evoked.crop(tmin=0, tmax=0.3)
 # Handling forward solution
 forward = mne.read_forward_solution(fwd_fname)
 

examples/inverse/plot_multidict_reweighted_tfmxne.py

@@ -56,7 +56,7 @@
                     reject=reject, preload=True)
 evoked = epochs.filter(1, None).average()
 evoked = evoked.pick_types(meg=True)
-evoked.crop(tmin=0.008, tmax=0.2, verbose='error')  # ignore baseline
+evoked.crop(tmin=0.008, tmax=0.2)
 
 # Compute noise covariance matrix
 cov = mne.compute_covariance(epochs, rank='info', tmax=0.)
@@ -85,8 +85,8 @@
 # Crop to remove edges
 for dip in dipoles:
     dip.crop(tmin=-0.05, tmax=0.3)
-evoked.crop(tmin=-0.05, tmax=0.3, verbose='error')
-residual.crop(tmin=-0.05, tmax=0.3, verbose='error')
+evoked.crop(tmin=-0.05, tmax=0.3)
+residual.crop(tmin=-0.05, tmax=0.3)
 
 
 ###############################################################################

examples/inverse/plot_rap_music.py

@@ -37,7 +37,7 @@
 evoked = mne.read_evokeds(evoked_fname, condition=condition,
                           baseline=(None, 0))
 # select N100
-evoked.crop(tmin=0.05, tmax=0.15, verbose='error')  # ignore baseline
+evoked.crop(tmin=0.05, tmax=0.15)
 
 evoked.pick_types(meg=True, eeg=False)
 

examples/inverse/plot_vector_mne_solution.py

@@ -1,4 +1,7 @@
 """
+
+.. _ex-vector-mne-solution:
+
 ============================================
 Plotting the full vector-valued MNE solution
 ============================================

examples/visualization/plot_montage.py → examples/visualization/montage.py

@@ -6,7 +6,7 @@
 ======================================
 
 This example illustrates how to load all the EEG system montages
-shipped in MNE-python, and display it on fsaverage template.
+shipped in MNE-python, and display it on the fsaverage template subject.
 """  # noqa: D205, D400
 # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
 #          Joan Massich <mailsik@gmail.com>

examples/visualization/plot_make_report.py

@@ -38,7 +38,7 @@
 # Load the evoked data
 evoked = read_evokeds(evoked_fname, condition='Left Auditory',
                       baseline=(None, 0), verbose=False)
-evoked.crop(0, .2, verbose='error')   # ignore baseline
+evoked.crop(0, .2)
 times = evoked.times[::4]
 # Create a list of figs for the slider
 figs = list()

mne/preprocessing/maxwell.py

@@ -1609,8 +1609,8 @@ def _prep_fine_cal(info, fine_cal):
     from ._fine_cal import read_fine_calibration
     _validate_type(fine_cal, (dict, 'path-like'))
     if not isinstance(fine_cal, dict):
-        fine_cal = read_fine_calibration(fine_cal)
         extra = op.basename(str(fine_cal))
+        fine_cal = read_fine_calibration(fine_cal)
     else:
         extra = 'dict'
     logger.info(f'    Using fine calibration {extra}')

mne/preprocessing/tests/test_maxwell.py

@@ -643,9 +643,13 @@ def test_fine_calibration():
 
     # Test 1D SSS fine calibration
     with use_coil_def(elekta_def_fname):
-        raw_sss = maxwell_filter(raw, calibration=fine_cal_fname,
-                                 origin=mf_head_origin, regularize=None,
-                                 bad_condition='ignore')
+        with catch_logging() as log:
+            raw_sss = maxwell_filter(raw, calibration=fine_cal_fname,
+                                     origin=mf_head_origin, regularize=None,
+                                     bad_condition='ignore', verbose=True)
+    log = log.getvalue()
+    assert 'Using fine calibration' in log
+    assert op.basename(fine_cal_fname) in log
     assert_meg_snr(raw_sss, sss_fine_cal, 82, 611)
     py_cal = raw_sss.info['proc_history'][0]['max_info']['sss_cal']
     assert (py_cal is not None)

tutorials/intro/plot_40_sensor_locations.py

@@ -223,8 +223,7 @@
 # keywords like ``'head'``, ``'outer_skull'``, or ``'brain'`` to the
 # ``surfaces`` parameter) making it useful for :ref:`assessing coordinate frame
 # transformations <plot_source_alignment>`. For examples of various uses of
-# :func:`~mne.viz.plot_alignment`, see
-# :doc:`../../auto_examples/visualization/plot_montage`,
+# :func:`~mne.viz.plot_alignment`, see :ref:`plot_montage`,
 # :doc:`../../auto_examples/visualization/plot_eeg_on_scalp`, and
 # :doc:`../../auto_examples/visualization/plot_meg_sensors`.
 #

tutorials/misc/plot_report.py

@@ -70,7 +70,7 @@
 path = mne.datasets.sample.data_path(verbose=False)
 report = mne.Report(verbose=True)
 report.parse_folder(path, pattern='*raw.fif', render_bem=False)
-report.save('report_basic.html')
+report.save('report_basic.html', overwrite=True)
 
 ###############################################################################
 # This report yields a textual summary of the :class:`~mne.io.Raw` files
@@ -85,7 +85,7 @@
 pattern = 'sample_audvis_filt-0-40_raw.fif'
 report = mne.Report(raw_psd=True, projs=True, verbose=True)
 report.parse_folder(path, pattern=pattern, render_bem=False)
-report.save('report_raw_psd.html')
+report.save('report_raw_psd.html', overwrite=True)
 
 ###############################################################################
 # The sample dataset also contains SSP projectors stored as *individual files*.
@@ -98,7 +98,7 @@
 pattern = 'sample_audvis_*proj.fif'
 report = mne.Report(info_fname=info_fname, verbose=True)
 report.parse_folder(path, pattern=pattern, render_bem=False)
-report.save('report_proj.html')
+report.save('report_proj.html', overwrite=True)
 
 ###############################################################################
 # This time we'll pass a specific ``subject`` and ``subjects_dir`` (even though
@@ -111,7 +111,7 @@
 subjects_dir = os.path.join(path, 'subjects')
 report = mne.Report(subject='sample', subjects_dir=subjects_dir, verbose=True)
 report.parse_folder(path, pattern='', mri_decim=25)
-report.save('report_mri_bem.html')
+report.save('report_mri_bem.html', overwrite=True)
 
 ###############################################################################
 # Now let's look at how :class:`~mne.Report` handles :class:`~mne.Evoked` data
@@ -122,7 +122,7 @@
 pattern = 'sample_audvis-no-filter-ave.fif'
 report = mne.Report(verbose=True)
 report.parse_folder(path, pattern=pattern, render_bem=False)
-report.save('report_evoked.html')
+report.save('report_evoked.html', overwrite=True)
 
 ###############################################################################
 # You have probably noticed that the EEG recordings look particularly odd. This
@@ -142,7 +142,7 @@
 pattern = 'sample_audvis-no-filter-ave.fif'
 report = mne.Report(baseline=baseline, verbose=True)
 report.parse_folder(path, pattern=pattern, render_bem=False)
-report.save('report_evoked_baseline.html')
+report.save('report_evoked_baseline.html', overwrite=True)
 
 ###############################################################################
 # To render whitened :class:`~mne.Evoked` files with baseline correction, pass
@@ -154,7 +154,7 @@
 baseline = (None, 0)
 report = mne.Report(cov_fname=cov_fname, baseline=baseline, verbose=True)
 report.parse_folder(path, pattern=pattern, render_bem=False)
-report.save('report_evoked_whitened.html')
+report.save('report_evoked_whitened.html', overwrite=True)
 
 ###############################################################################
 # If you want to actually *view* the noise covariance in the report, make sure
@@ -168,7 +168,7 @@
 info_fname = os.path.join(path, 'MEG', 'sample', 'sample_audvis-ave.fif')
 report = mne.Report(info_fname=info_fname, verbose=True)
 report.parse_folder(path, pattern=pattern, render_bem=False)
-report.save('report_cov.html')
+report.save('report_cov.html', overwrite=True)
 
 ###############################################################################
 # Adding custom plots to a report
@@ -188,7 +188,7 @@
 
 # add the custom plot to the report:
 report.add_figs_to_section(fig, captions='Left Auditory', section='evoked')
-report.save('report_custom.html')
+report.save('report_custom.html', overwrite=True)
 
 ###############################################################################
 # Managing report sections
@@ -231,7 +231,7 @@
                                captions='Left Auditory',
                                section='evoked',
                                replace=True)
-    report.save('report_final.html')
+    report.save('report_final.html', overwrite=True)
 
 ###############################################################################
 # With the context manager, the updated report is also automatically saved

tutorials/source-modeling/plot_beamformer_lcmv.py

@@ -66,8 +66,8 @@
                     baseline=(None, 0), preload=True, proj=proj,
                     reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
 
-# for speed purposes, cut to a window of interest (ignore baseline, etc.)
-evoked = epochs.average().crop(0.05, 0.15, verbose='error')
+# for speed purposes, cut to a window of interest
+evoked = epochs.average().crop(0.05, 0.15)
 
 # Visualize averaged sensor space data
 evoked.plot_joint()

tutorials/source-modeling/plot_compute_covariance.py

@@ -173,7 +173,8 @@
 
 ##############################################################################
 # Based on the negative log-likelihood, the baseline covariance
-# seems more appropriate.
+# seems more appropriate. See :ref:`ex-covariance-whitening-dspm` for more
+# information.
 
 ###############################################################################
 # References

tutorials/source-modeling/plot_dipole_fit.py

@@ -37,7 +37,7 @@
                           baseline=(None, 0))
 evoked.pick_types(meg=True, eeg=False)
 evoked_full = evoked.copy()
-evoked.crop(0.07, 0.08, verbose='error')  # ignore baseline
+evoked.crop(0.07, 0.08)
 
 # Fit a dipole
 dip = mne.fit_dipole(evoked, fname_cov, fname_bem, fname_trans)[0]

tutorials/source-modeling/plot_forward.py

@@ -198,7 +198,7 @@
 # parameter.
 
 fwd = mne.make_forward_solution(raw_fname, trans=trans, src=src, bem=bem,
-                                meg=True, eeg=False, mindist=5.0, n_jobs=2,
+                                meg=True, eeg=False, mindist=5.0, n_jobs=1,
                                 verbose=True)
 print(fwd)