FIX: Minor fixes [circle full]

Author: larsoner

doc/conf.py

@@ -368,8 +368,8 @@
 ##############################################################################
 # sphinx-gallery
 
-examples_dirs = ['../examples', '../tutorials']
-gallery_dirs = ['auto_examples', 'auto_tutorials']
+examples_dirs = ['../tutorials', '../examples']
+gallery_dirs = ['auto_tutorials', 'auto_examples']
 os.environ['_MNE_BUILDING_DOC'] = 'true'
 
 scrapers = ('matplotlib',)

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/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)
 

tutorials/stats-source-space/plot_stats_cluster_time_frequency_repeated_measures_anova.py

@@ -156,7 +156,7 @@
                times[-1], freqs[0], freqs[-1]], aspect='auto',
                origin='lower')
     # create mask for significant Time-frequency locations
-    effect = np.ma.masked_array(effect, [sig > .05])
+    effect[sig >= 0.05] = np.nan
     plt.imshow(effect.reshape(8, 211), cmap='RdBu_r', extent=[times[0],
                times[-1], freqs[0], freqs[-1]], aspect='auto',
                origin='lower')
@@ -203,8 +203,8 @@ def stat_fun(*args):
 # Create new stats image with only significant clusters:
 
 good_clusters = np.where(cluster_p_values < .05)[0]
-T_obs_plot = np.ma.masked_array(T_obs,
-                                np.invert(clusters[np.squeeze(good_clusters)]))
+T_obs_plot = T_obs.copy()
+T_obs_plot[~clusters[np.squeeze(good_clusters)]] = np.nan
 
 plt.figure()
 for f_image, cmap in zip([T_obs, T_obs_plot], [plt.cm.gray, 'RdBu_r']):
@@ -221,10 +221,13 @@ def stat_fun(*args):
 # Now using FDR:
 
 mask, _ = fdr_correction(pvals[2])
-T_obs_plot2 = np.ma.masked_array(T_obs, np.invert(mask))
+T_obs_plot2 = T_obs.copy()
+T_obs_plot2[~mask.reshape(T_obs_plot.shape)] = np.nan
 
 plt.figure()
 for f_image, cmap in zip([T_obs, T_obs_plot2], [plt.cm.gray, 'RdBu_r']):
+    if np.isnan(f_image).all():
+        continue  # nothing to show
     plt.imshow(f_image, cmap=cmap, extent=[times[0], times[-1],
                freqs[0], freqs[-1]], aspect='auto',
                origin='lower')