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        "%matplotlib inline"
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      "source": [
        "\n# Morph source estimates from one subject to another subject\n\n\nA source estimate from a given subject 'sample' is morphed\nto the anatomy of another subject 'fsaverage'. The output\nis a source estimate defined on the anatomy of 'fsaverage'\n\n\n"
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      "source": [
        "# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>\n#          Eric Larson <larson.eric.d@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()\n\nsubject_from = 'sample'\nsubject_to = 'fsaverage'\nsubjects_dir = data_path + '/subjects'\n\nfname = data_path + '/MEG/sample/sample_audvis-meg'\n\n# Read input stc file\nstc_from = mne.read_source_estimate(fname)\n# Morph using one method (supplying the vertices in fsaverage's source\n# space makes it faster). Note that for any generic subject, you could do:\n#     vertices_to = mne.grade_to_vertices(subject_to, grade=5)\n# But fsaverage's source space was set up so we can just do this:\nvertices_to = [np.arange(10242), np.arange(10242)]\nstc_to = mne.morph_data(subject_from, subject_to, stc_from, n_jobs=1,\n                        grade=vertices_to, subjects_dir=subjects_dir)\nstc_to.save('%s_audvis-meg' % subject_to)\n\n# Morph using another method -- useful if you're going to do a lot of the\n# same inter-subject morphing operations; you could save and load morph_mat\nmorph_mat = mne.compute_morph_matrix(subject_from, subject_to,\n                                     stc_from.vertices, vertices_to,\n                                     subjects_dir=subjects_dir)\nstc_to_2 = mne.morph_data_precomputed(subject_from, subject_to,\n                                      stc_from, vertices_to, morph_mat)\nstc_to_2.save('%s_audvis-meg_2' % subject_to)\n\n# View source activations\nplt.plot(stc_from.times, stc_from.data.mean(axis=0), 'r', label='from')\nplt.plot(stc_to.times, stc_to.data.mean(axis=0), 'b', label='to')\nplt.plot(stc_to_2.times, stc_to.data.mean(axis=0), 'g', label='to_2')\nplt.xlabel('time (ms)')\nplt.ylabel('Mean Source amplitude')\nplt.legend()\nplt.show()"
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