FIX: Fix grand average

.gitignore

@@ -43,6 +43,7 @@ doc/build/
 doc/auto_examples/
 doc/modules/generated/
 doc/source/generated/
+doc/source/modules
 pip-log.txt
 .coverage
 tags

doc/sphinxext/gen_rst.py

@@ -73,6 +73,8 @@ def execfile(filename, global_vars=None, local_vars=None):
 
 
 import joblib
+MAX_NB_LINES_STDOUT = 20
+
 
 ###############################################################################
 # A tee object to redict streams to multiple outputs
@@ -945,8 +947,12 @@ def generate_file_rst(fname, target_dir, src_dir, root_dir, plot_gallery):
                         '')
                 my_stdout = my_stdout.strip().expandtabs()
                 if my_stdout:
+                    output_lines = my_stdout.split('\n')
+                    if len(output_lines) > MAX_NB_LINES_STDOUT:
+                        output_lines = output_lines[:MAX_NB_LINES_STDOUT]
+                        output_lines.append('...')
                     stdout = '**Script output**::\n\n  %s\n\n' % (
-                        '\n  '.join(my_stdout.split('\n')))
+                        '\n  '.join(output_lines))
                 open(stdout_path, 'w').write(stdout)
                 open(time_path, 'w').write('%f' % time_elapsed)
                 os.chdir(cwd)
@@ -976,6 +982,7 @@ def generate_file_rst(fname, target_dir, src_dir, root_dir, plot_gallery):
                     scale_image(image_path % fig_mngr.num, 850)
                     figure_list.append(image_fname % fig_mngr.num)
                     last_fig_num = fig_mngr.num
+                plt.close('all')
                 e = mlab.get_engine()
                 for scene in e.scenes:
                     last_fig_num += 1
@@ -984,6 +991,7 @@ def generate_file_rst(fname, target_dir, src_dir, root_dir, plot_gallery):
                     scale_image(image_path % last_fig_num, 850)
                     figure_list.append(image_fname % last_fig_num)
                     mlab.close(scene)
+                mlab.close(all=True)
 
             except Exception:
                 print(80 * '_')

examples/inverse/plot_gamma_map_inverse.py

@@ -50,7 +50,8 @@
 scale_factors = np.max(np.abs(stc.data), axis=1)
 scale_factors = 0.5 * (1 + scale_factors / np.max(scale_factors))
 
-plot_sparse_source_estimates(forward['src'], stc, bgcolor=(1, 1, 1),
+plot_sparse_source_estimates(
+    forward['src'], stc, bgcolor=(1, 1, 1),
     modes=['sphere'], opacity=0.1, scale_factors=(scale_factors, None),
     fig_name="Gamma-MAP")
 

mne/evoked.py

@@ -938,7 +938,7 @@ def _get_evoked_node(fname):
     return evoked_node
 
 
-def grand_average(all_evokeds, interpolate_bads='eeg'):
+def grand_average(all_evoked, interpolate_bads='eeg'):
     """Make grand average of a list evoked data
 
     The function interpolates bad channels based on `interpolate_bads`
@@ -968,29 +968,29 @@ def grand_average(all_evokeds, interpolate_bads='eeg'):
         The grand average data.
     """
     # check if all elements in the given list are evoked data
-    if not all(isinstance(e, Evoked) for e in all_evokeds):
+    if not all(isinstance(e, Evoked) for e in all_evoked):
         raise ValueError("Not all the elements in list are evoked data")
 
     if interpolate_bads != 'eeg':
         raise ValueError('Only EEG channels can be interpolated currently.')
 
     # Copy channels to leave the original evoked datasets intact.
-    all_evokeds = [e.copy() for e in all_evokeds]
+    all_evoked = [e.copy() for e in all_evoked]
 
     # Interpolates if necessary
     if interpolate_bads == 'eeg':
-        all_evokeds = [e.interpolate_bads_eeg() if len(e.info['bads']) > 0
-                       else e for e in all_evokeds]
+        all_evoked = [e.interpolate_bads_eeg() if len(e.info['bads']) > 0
+                      else e for e in all_evoked]
 
     # change and ignore the nave for all evoked datasets.
-    for e in all_evokeds:
+    for e in all_evoked:
         e.nave = 1
 
-    equalize_channels(all_evokeds)  # apply equalize_channels
+    equalize_channels(all_evoked)  # apply equalize_channels
     # make grand_average object using merge_evoked
-    grand_average = merge_evoked(all_evokeds)
+    grand_average = merge_evoked(all_evoked)
     # change the grand_average.nave to the number of Evokeds
-    grand_average.nave = len(all_evokeds)
+    grand_average.nave = len(all_evoked)
     # change comment field
     grand_average.comment = "Grand average (n = %d)" % grand_average.nave
     return grand_average

mne/viz/_3d.py

@@ -517,6 +517,8 @@ def plot_sparse_source_estimates(src, stcs, colors=None, linewidth=2,
                                  opacity=0.2, brain_color=(0.7,) * 3,
                                  show=True, high_resolution=False,
                                  fig_name=None, fig_number=None, labels=None,
+                                 modes=('cone', 'sphere'),
+                                 scale_factors=(1, 0.6),
                                  verbose=None, **kwargs):
     """Plot source estimates obtained with sparse solver
 
@@ -555,13 +557,21 @@ def plot_sparse_source_estimates(src, stcs, colors=None, linewidth=2,
         label and the waveforms within each cluster are presented in
         the same color. labels should be a list of ndarrays when
         stcs is a list ie. one label for each stc.
+    modes : list
+        Should be a list, with each entry being ``'cone'`` or ``'sphere'``
+        to specify how the dipoles should be shown.
+    scale_factros : list
+        List of floating point scale factors for the markers.
     verbose : bool, str, int, or None
         If not None, override default verbose level (see mne.verbose).
     kwargs : kwargs
         Keyword arguments to pass to mlab.triangular_mesh.
     """
-    modes = ['cone', 'sphere']
-    scale_factors = [1, 0.6]
+    known_modes = ['cone', 'sphere']
+    if not isinstance(modes, (list, tuple)) or \
+            not all(mode in known_modes for mode in modes):
+        raise ValueError('mode must be a list containing only '
+                         '"cone" or "sphere"')
     if not isinstance(stcs, list):
         stcs = [stcs]
     if labels is not None and not isinstance(labels, list):