Merge e4c1c6a into 1eeddd3

examples/time_frequency/plot_time_frequency_simulated.py

@@ -101,7 +101,7 @@
     power = tfr_stockwell(epochs, fmin=fmin, fmax=fmax, width=width)
     power.plot([0], baseline=(0., 0.1), mode='mean',
                title='Sim: Using S transform, width '
-                     '= {:0.1f}'.format(width), show=True)
+                     '= {0:0.1f}'.format(width), show=True)
 
 # #############################################################################
 # Finally, compare to morlet wavelet

mne/channels/channels.py

@@ -220,7 +220,7 @@ def _get_channel_positions(self, picks=None):
         n_zero = np.sum(np.sum(np.abs(pos), axis=1) == 0)
         if n_zero > 1:  # XXX some systems have origin (0, 0, 0)
             raise ValueError('Could not extract channel positions for '
-                             '{} channels'.format(n_zero))
+                             '{0} channels'.format(n_zero))
         return pos
 
     def _set_channel_positions(self, pos, names):
@@ -771,7 +771,7 @@ def read_ch_connectivity(fname, picks=None):
                 break
         else:
             raise ValueError('I do not know about this neighbor '
-                             'template: "{}"'.format(fname))
+                             'template: "{0}"'.format(fname))
 
         fname = op.join(templates_dir, fname)
 
@@ -783,8 +783,8 @@ def read_ch_connectivity(fname, picks=None):
     if picks is not None:
         if max(picks) >= len(ch_names):
             raise ValueError('The picks must be compatible with '
-                             'channels. Found a pick ({}) which exceeds '
-                             'the channel range ({})'
+                             'channels. Found a pick ({0}) which exceeds '
+                             'the channel range ({1})'
                              .format(max(picks), len(ch_names)))
     connectivity = _ch_neighbor_connectivity(ch_names, neighbors)
     if picks is not None:

mne/decoding/tests/test_time_gen.py

@@ -59,13 +59,14 @@ def test_generalization_across_time():
     y_4classes = np.hstack((epochs.events[:7, 2], epochs.events[7:, 2] + 1))
     if check_version('sklearn', '0.18'):
         from sklearn.model_selection import (KFold, StratifiedKFold,
-                                             ShuffleSplit, LeaveOneLabelOut)
+                                             ShuffleSplit, LeaveOneGroupOut)
         cv_shuffle = ShuffleSplit()
-        cv = LeaveOneLabelOut()
+
+        cv = LeaveOneGroupOut()
         # XXX we cannot pass any other parameters than X and y to cv.split
         # so we have to build it before hand
         cv_lolo = [(train, test) for train, test in cv.split(
-                   X=y_4classes, y=y_4classes, labels=y_4classes)]
+                   X=y_4classes, y=y_4classes, groups=y_4classes)]
 
         # With sklearn >= 0.17, `clf` can be identified as a regressor, and
         # the scoring metrics can therefore be automatically assigned.

mne/io/pick.py

@@ -272,7 +272,7 @@ def pick_types(info, meg=True, eeg=False, stim=False, eog=False, ecg=False,
                   ias, syst, seeg, dipole, gof, bio, ecog):
         if not isinstance(param, bool):
             w = ('Parameters for all channel types (with the exception '
-                 'of "meg" and "ref_meg") must be of type bool, not {}.')
+                 'of "meg" and "ref_meg") must be of type bool, not {0}.')
             raise ValueError(w.format(type(param)))
 
     for k in range(nchan):

mne/tests/test_source_estimate.py

@@ -149,7 +149,7 @@ def test_volume_stc():
         assert_array_almost_equal(img.get_affine(), t1_img.get_affine(),
                                   decimal=5)
 
-    except ImportError:
+    except Exception:
         print('Save as nifti test skipped, needs NiBabel')
 
 

mne/time_frequency/tfr.py

@@ -774,10 +774,8 @@ def _onselect(self, eclick, erelease, baseline, mode, layout):
         if 'grad' in self:
             types.append('grad')
         fig = figure_nobar()
-        fig.suptitle('{:.2f} s - {:.2f} s, {:.2f} Hz - {:.2f} Hz'.format(tmin,
-                                                                         tmax,
-                                                                         fmin,
-                                                                         fmax),
+        fig.suptitle('{0:.2f} s - {1:.2f} s, '
+                     '{2:.2f} Hz - {3:.2f} Hz'.format(tmin, tmax, fmin, fmax),
                      y=0.04)
         for idx, ch_type in enumerate(types):
             ax = plt.subplot(1, len(types), idx + 1)

mne/utils.py

@@ -682,7 +682,7 @@ def has_nibabel(vox2ras_tkr=False):
                                    'header_class', 0),
                            'get_vox2ras_tkr', None) is not None)
         return out
-    except ImportError:
+    except Exception:
         return False
 
 

setup.cfg

@@ -29,4 +29,4 @@ doctest-fixtures = _fixture
 
 [flake8]
 exclude = __init__.py,*externals*,constants.py,fixes.py
-ignore = E241
+ignore = E241,E305

tutorials/plot_dipole_fit.py

@@ -75,7 +75,7 @@
 plot_params['colorbar'] = True
 diff.plot_topomap(time_format='Difference', axes=axes[2], **plot_params)
 plt.suptitle('Comparison of measured and predicted fields '
-             'at {:.0f} ms'.format(best_time * 1000.), fontsize=16)
+             'at {0:.0f} ms'.format(best_time * 1000.), fontsize=16)
 
 ###############################################################################
 # Estimate the time course of a single dipole with fixed position and

tutorials/plot_python_intro.py

@@ -34,7 +34,7 @@
 # If you come from a background of matlab, remember that indexing in python
 # starts from zero:
 a = [1, 2, 3, 4]
-print('This is the zeroth value in the list: {}'.format(a[0]))
+print('This is the zeroth value in the list: {0}'.format(a[0]))
 
 ###############################################################################
 # No need to reinvent the wheel. Scipy and Numpy are battle field tested

tutorials/plot_stats_spatio_temporal_cluster_sensors.py

@@ -70,8 +70,8 @@
 
 plt.imshow(connectivity.toarray(), cmap='gray', origin='lower',
            interpolation='nearest')
-plt.xlabel('{} Magnetometers'.format(len(ch_names)))
-plt.ylabel('{} Magnetometers'.format(len(ch_names)))
+plt.xlabel('{0} Magnetometers'.format(len(ch_names)))
+plt.ylabel('{0} Magnetometers'.format(len(ch_names)))
 plt.title('Between-sensor adjacency')
 
 ###############################################################################