.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_paper/1_compute_parcs.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_paper_1_compute_parcs.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_paper_1_compute_parcs.py:


Generate MEG informed cortical parcellations
============================================

.. GENERATED FROM PYTHON SOURCE LINES 6-101

.. code-block:: Python



    import os
    import os.path as op
    import numpy as np
    import pickle

    from mne import (read_forward_solution, pick_types_forward,
                     convert_forward_solution, read_labels_from_annot)

    from megicparc import compute_distance_matrix, compute_parcellation

    target = '../data'
    subjects = ['k1_T1', 'k2_T1', 'k4_T1', 'k6_T1', 'k7_T1',
               'CC110045', 'CC110182', 'CC110174', 'CC110126', 'CC110056']

    subjects_dir = op.join(target, 'subjects_flame')
    sensors_meg = 'grad'
    string_lf = op.join(target, 'fwd_models', 'original_fwd',
                      '{:s}_meg_single_layer-fwd.fif')

    gamma_tot = np.arange(0, 1.01, 0.2)
    knn_tot = [10, 20, 30, 40]
    theta = 0.05
    parc = 'aparc'

    folder_fl = op.join(target, 'results', 'parcellations')
    string_target_file = op.join(folder_fl,
                            '{:s}_flame_grad_k{:d}_gamma{:1.2f}_theta{:1.2f}.pkl')

    if not op.isdir(folder_fl):
        os.mkdir(folder_fl)

    # In[] Initialization
    for idx_sub, subject in enumerate(subjects):
        print('Working with %s' % subject)

        # In[]: Step 2. Load
        #   2.a. Forward model
        path_lf = string_lf.format(subject)
        fwd = read_forward_solution(path_lf)
        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)

        #   2.b. Anatomical regions
        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

        # In[]: Step 3. Run flame
        for gamma in gamma_tot:
            sort_dist = compute_distance_matrix(fwd, gamma=gamma,
                                                theta=theta, labels=label)
            for knn in knn_tot:
                target_file = string_target_file.format(
                    subject, knn, gamma, theta)
                flame_data = compute_parcellation(sort_dist, k_nn=knn)
                # - Save
                print('Saving %s' % target_file)
                aux_f = open(target_file, 'wb')
                pickle.dump(flame_data, aux_f, protocol=2)
                aux_f.close()

                del flame_data
            del sort_dist

        if subject == 'k2_T1':  # For 'k2_T1' we also compute parcellations for theta = 0

            theta_aux = 0

            for gamma in gamma_tot:
                sort_dist = compute_distance_matrix(fwd, gamma=gamma,
                                                    theta=theta_aux, labels=label)
                for knn in knn_tot:
                    target_file = string_target_file.format(
                        subject, knn, gamma, theta_aux)
                    flame_data = compute_parcellation(sort_dist, k_nn=knn)
                    # - Save
                    print('Saving %s' % target_file)
                    aux_f = open(target_file, 'wb')
                    pickle.dump(flame_data, aux_f, protocol=2)
                    aux_f.close()

                    del flame_data
                del sort_dist








.. _sphx_glr_download_auto_paper_1_compute_parcs.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 1_compute_parcs.ipynb <1_compute_parcs.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: 1_compute_parcs.py <1_compute_parcs.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_