.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_1_generate_parc.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_1_generate_parc.py: Create a MEG informed cortical parcellation =========================================== This example shows how to generate and visualize a lead-field based MEG informed cortical parcellation. .. GENERATED FROM PYTHON SOURCE LINES 8-9 Import the required .. GENERATED FROM PYTHON SOURCE LINES 9-21 .. code-block:: Python from megicparc import (compute_distance_matrix, compute_parcellation, store_flame_labels) from megicparc.viz import (plot_flame_labels, plot_flame_centroids) from mne import (read_forward_solution, read_source_spaces, read_labels_from_annot, pick_types_forward, convert_forward_solution) from mne.datasets import sample import os.path as op .. GENERATED FROM PYTHON SOURCE LINES 22-23 Define input parameters for the flame algorithm running in megicperc .. GENERATED FROM PYTHON SOURCE LINES 23-30 .. code-block:: Python parc = 'aparc' gamma = 0.8 theta = 0.05 sensors_meg = 'grad' knn = 30 .. GENERATED FROM PYTHON SOURCE LINES 31-32 Load lead-field matrix and source-space .. GENERATED FROM PYTHON SOURCE LINES 32-52 .. code-block:: Python data_path = sample.data_path() subjects_dir = op.join(data_path, 'subjects') subject = 'sample' fwd_file = op.join(data_path, 'MEG', subject, 'sample_audvis-meg-eeg-oct-6-fwd.fif') src_file = op.join('..', 'data', 'data_mne_sample', 'source_space_distance-src.fif') fwd = read_forward_solution(fwd_file) fwd = pick_types_forward(fwd, meg=sensors_meg, eeg=False, ref_meg=False, exclude='bads') fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True, use_cps=True) src = read_source_spaces(src_file) # Inter-source distances along the cortical surface has been added # to the source-space as follows #from mne import add_source_space_distances #src = add_source_space_distances(fwd['src']) #src.save(op.join('..', 'data', 'data_mne_sample', 'source_space_distance-src.fif')) fwd['src'] = src .. rst-class:: sphx-glr-script-out .. code-block:: none Reading forward solution from /u/29/sommars1/unix/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif... Reading a source space... Computing patch statistics... Patch information added... Distance information added... [done] Reading a source space... Computing patch statistics... Patch information added... Distance information added... [done] 2 source spaces read Desired named matrix (kind = 3523) not available Read MEG forward solution (7498 sources, 306 channels, free orientations) Desired named matrix (kind = 3523) not available Read EEG forward solution (7498 sources, 60 channels, free orientations) Forward solutions combined: MEG, EEG Source spaces transformed to the forward solution coordinate frame 203 out of 366 channels remain after picking Average patch normals will be employed in the rotation to the local surface coordinates.... Converting to surface-based source orientations... [done] Reading a source space... Computing patch statistics... Patch information added... Distance information added... [done] Reading a source space... Computing patch statistics... Patch information added... Distance information added... [done] 2 source spaces read .. GENERATED FROM PYTHON SOURCE LINES 53-54 Load the cortical atlas and run flame algorithm .. GENERATED FROM PYTHON SOURCE LINES 54-88 .. code-block:: Python label_lh = read_labels_from_annot(subject=subject, parc=parc, hemi='lh', subjects_dir=subjects_dir) label_rh = read_labels_from_annot(subject=subject, parc=parc, hemi='rh', subjects_dir=subjects_dir) label = label_lh + label_rh sort_dist = compute_distance_matrix(fwd, gamma=gamma, theta=theta, labels=label) sample_parc = compute_parcellation(sort_dist, k_nn=knn) # Store megic parcels as mne-python labels for visualization purpose. sample_parc_labels = store_flame_labels(sample_parc, src, subject) "" plot_flame_centroids(sample_parc, fwd['src'], subject, subjects_dir, brain=None, surf='inflated', scale_factor=0.5, color='white') "" brain_parc = plot_flame_labels([87], sample_parc_labels, src, subject, subjects_dir, surf='inflated', brain=None, color=None, plot_region=True, plot_points=False, plot_borders=False) plot_flame_labels([80], sample_parc_labels, src, subject, subjects_dir, surf='inflated', brain=brain_parc, color=None, plot_region=False, plot_points=False, plot_borders=True) plot_flame_labels([52], sample_parc_labels, src, subject, subjects_dir, surf='inflated', brain=brain_parc, color=None, plot_region=False, plot_points=True, plot_borders=False) "" # sphinx_gallery_thumbnail_number = 2 .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/images/sphx_glr_plot_1_generate_parc_001.png :alt: plot 1 generate parc :srcset: /auto_examples/images/sphx_glr_plot_1_generate_parc_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/images/sphx_glr_plot_1_generate_parc_002.png :alt: plot 1 generate parc :srcset: /auto_examples/images/sphx_glr_plot_1_generate_parc_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none Reading labels from parcellation... read 34 labels from /u/29/sommars1/unix/mne_data/MNE-sample-data/subjects/sample/label/lh.aparc.annot Reading labels from parcellation... read 34 labels from /u/29/sommars1/unix/mne_data/MNE-sample-data/subjects/sample/label/rh.aparc.annot Using leadfield of size = (203, 7498) Computing distance matrix for gamma=0.80 theta=0.05 Reading cortical distance from src Adding anatomical constraints Computing cosine distance Normalizing leadfield for grad sensors Check n_vert = 7498 1 6.157426368290271 2 3.7167014692484543 3 2.7595568820368537 4 2.1770745993405196 5 1.7817718557087614 6 1.4964028907957063 7 1.2810666930290426 8 1.1129196110845063 9 0.9779730199638816 10 0.8672296083797089 11 0.7746699263117646 12 0.6961309771802204 13 0.6286492641904173 14 0.5700602769336963 15 0.5187461067810686 16 0.4734718460760014 17 0.43327687565705086 18 0.3974010025990008 19 0.3652332493560549 20 0.33627567664175245 21 0.31011737542352164 22 0.28641545879157715 23 0.26488095130827444 24 0.2452681578848642 25 0.22736654105904522 26 0.21099443194069117 27 0.19599409960543274 28 0.18222783986229002 29 0.16957483842881182 30 0.15792862939397795 31 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1.121727634112295e-06 266 1.0727281590199096e-06 267 1.0258855385491105e-06 268 9.811039759628494e-07 Using pyvistaqt 3d backend. '' .. rst-class:: sphx-glr-timing **Total running time of the script:** (6 minutes 37.613 seconds) .. _sphx_glr_download_auto_examples_plot_1_generate_parc.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_1_generate_parc.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_1_generate_parc.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_