old_contents: """
  ==========================================================
  Morph source estimates from one subject to another subject
  ==========================================================
  
  A source estimate from a given subject 'sample' is morphed
  to the anatomy of another subject 'fsaverage'. The output
  is a source estimate defined on the anatomy of 'fsaverage'
  
  """
  # Authors: Alexandre Gramfort <gramfort@nmr.mgh.harvard.edu>
  #          Eric Larson <larson.eric.d@gmail.com>
  #
  # License: BSD (3-clause)
  
  print __doc__
  
  import mne
  import numpy as np
  from mne.datasets import sample
  
  data_path = sample.data_path()
  
  subject_from = 'sample'
  subject_to = 'fsaverage'
  
  fname = data_path + '/MEG/sample/sample_audvis-meg'
  src_fname = data_path + '/MEG/sample/sample_audvis-meg-oct-6-fwd.fif'
  
  # Read input stc file
  stc_from = mne.read_source_estimate(fname)
  # Morph using one method (supplying the vertices in fsaverage's source
  # space makes it faster). Note that for any generic subject, you could do:
  #     vertices_to = mne.grade_to_vertices(subject_to, grade=5)
  # But fsaverage's source space was set up so we can just do this:
  vertices_to = [np.arange(10242), np.arange(10242)]
  stc_to = mne.morph_data(subject_from, subject_to, stc_from, n_jobs=1,
                          grade=vertices_to)
  stc_to.save('%s_audvis-meg' % subject_to)
  
  # Morph using another method -- useful if you're going to do a lot of the
  # same inter-subject morphing operations; you could save and load morph_mat
  morph_mat = mne.compute_morph_matrix(subject_from, subject_to, stc_from.vertno,
                                       vertices_to)
  stc_to_2 = mne.morph_data_precomputed(subject_from, subject_to,
                                        stc_from, vertices_to, morph_mat)
  stc_to_2.save('%s_audvis-meg_2' % subject_to)
  
  # View source activations
  import pylab as pl
  pl.plot(stc_from.times, stc_from.data.mean(axis=0), 'r', label='from');
  pl.plot(stc_to.times, stc_to.data.mean(axis=0), 'b', label='to');
  pl.plot(stc_to_2.times, stc_to.data.mean(axis=0), 'g', label='to_2');
  pl.xlabel('time (ms)');
  pl.ylabel('Mean Source amplitude');
  pl.legend();
  pl.show();
  
new_contents: """
  ==========================================================
  Morph source estimates from one subject to another subject
  ==========================================================
  
  A source estimate from a given subject 'sample' is morphed
  to the anatomy of another subject 'fsaverage'. The output
  is a source estimate defined on the anatomy of 'fsaverage'
  
  """
  # Authors: Alexandre Gramfort <gramfort@nmr.mgh.harvard.edu>
  #          Eric Larson <larson.eric.d@gmail.com>
  #
  # License: BSD (3-clause)
  
  print __doc__
  
  import mne
  import numpy as np
  from mne.datasets import sample
  
  data_path = sample.data_path()
  
  subject_from = 'sample'
  subject_to = 'fsaverage'
  
  fname = data_path + '/MEG/sample/sample_audvis-meg'
  src_fname = data_path + '/MEG/sample/sample_audvis-meg-oct-6-fwd.fif'
  
  # Read input stc file
  stc_from = mne.read_source_estimate(fname)
  # Morph using one method (supplying the vertices in fsaverage's source
  # space makes it faster). Note that for any generic subject, you could do:
  #     vertices_to = mne.grade_to_vertices(subject_to, grade=5)
  # But fsaverage's source space was set up so we can just do this:
  vertices_to = [np.arange(10242), np.arange(10242)]
  stc_to = mne.morph_data(subject_from, subject_to, stc_from, n_jobs=1,
                          grade=vertices_to)
  stc_to.save('%s_audvis-meg' % subject_to)
  
  # Morph using another method -- useful if you're going to do a lot of the
  # same inter-subject morphing operations; you could save and load morph_mat
  morph_mat = mne.compute_morph_matrix(subject_from, subject_to, stc_from.vertno,
                                       vertices_to)
  stc_to_2 = mne.morph_data_precomputed(subject_from, subject_to,
                                        stc_from, vertices_to, morph_mat)
  stc_to_2.save('%s_audvis-meg_2' % subject_to)
  
  # View source activations
  import pylab as pl
  pl.plot(stc_from.times, stc_from.data.mean(axis=0), 'r', label='from')
  pl.plot(stc_to.times, stc_to.data.mean(axis=0), 'b', label='to')
  pl.plot(stc_to_2.times, stc_to.data.mean(axis=0), 'g', label='to_2')
  pl.xlabel('time (ms)')
  pl.ylabel('Mean Source amplitude')
  pl.legend()
  pl.show()
  
subject: remove unintended sphinx directives