larsoner

# In fsaverage/mri ran:
#
# $ mri_aparc2aseg --s fsaverage --volmask --annot HCPMMP1
# $ mri_aparc2aseg --s fsaverage --volmask --annot HCPMMP1_combined
#
# Then this script can be used to create the lookup table for atlas_ids.

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

larsoner

import os
import time
from datetime import datetime, timezone, timedelta

import mne
import numpy as np
import h5py
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

larsoner

import os.path as op
import mne
from mne import compute_rank
from mne.beamformer import make_lcmv
data_path = mne.datasets.testing.data_path()
fname_raw = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif')
fname_fwd = op.join(data_path, 'MEG', 'sample',
                    'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif')

raw = mne.io.read_raw_fif(fname_raw).fix_mag_coil_types()

larsoner

# -*- coding: utf-8 -*-
"""
============================
Plot a cortical parcellation
============================

In this example, we download the HCP-MMP1.0 parcellation [1]_ and show it
on ``fsaverage``.
We will also download the customized 448-label aparc parcellation from [2]_

larsoner

# -*- coding: utf-8 -*-
"""Some utility functions."""
# Authors: Alexandre Gramfort <[email protected]>
#
# License: BSD (3-clause)

from collections.abc import Iterable
import time
import logging
import tempfile

larsoner

import numpy as np


###############################################################################
# Optimized SVD

def _check_transpose(x):
    if x.shape[-2] < x.shape[-1]:
        x = x.swapaxes(-2, -1)
        transpose = True

larsoner

def get_atlas_roi_mask(stc, roi, atlas='IXI', atlas_subject=None,
                       subjects_dir=None):
    """Get ROI mask for a given subject/atlas.
    Parameters
    ----------
    stc : instance of mne.SourceEstimate or mne.VectorSourceEstimate
        The source estimate.
    roi : str
        The ROI to obtain a mask for.
    atlas : str

larsoner

"""
export MRI=/mnt/bakraid/larsoner/mri/Infants/Sources/BEM/AVG7-5Months3T_segmented_BEM4.nii.gz
mri_binarize --i $MRI --o inner_skull.mgz --min 1 --max 2
mri_binarize --i $MRI --o outer_skull.mgz --min 1 --max 3
mri_binarize --i $MRI --o outer_skin.mgz --min 1 --max 4
mri_tessellate inner_skull.mgz 1 lh.inner_skull_dense
mri_tessellate outer_skull.mgz 1 lh.outer_skull_dense
mri_tessellate outer_skin.mgz 1 lh.outer_skin_dense
mris_extract_main_component lh.inner_skull_dense lh.inner_skull_dense
mris_extract_main_component lh.outer_skull_dense lh.outer_skull_dense

larsoner

import numpy as np
import matplotlib.pyplot as plt

rng = np.random.RandomState(0)
groups = ('TD', 'ASD')
conditions = ('space', 'both', 'pitch')
data = dict(  # create some fake data that will plot trends nicely
    TD=rng.rand(10, 3) + np.array([0, -0.5, 0.5]) + 1,
    ASD=rng.rand(12, 3) + np.array([0, 0.5, 0.]) + 1,
)

larsoner

import numpy as np
import matplotlib.pyplot as plt

import mne

data_path = mne.datasets.testing.data_path()
subjects_dir = data_path + '/subjects'
evoked = mne.read_evokeds(
    data_path + '/MEG/sample/sample_audvis-ave.fif')[0]
evoked.apply_baseline((None, 0))