def test_plot_bem():
"""Test plotting of BEM contours
"""
assert_raises(IOError, plot_bem, subject='bad-subject',
subjects_dir=subjects_dir)
assert_raises(ValueError, plot_bem, subject='sample',
subjects_dir=subjects_dir, orientation='bad-ori')
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='sagittal', slices=[25, 50])
def test_plot_bem():
"""Test plotting of BEM contours
"""
assert_raises(IOError, plot_bem, subject='bad-subject',
subjects_dir=subjects_dir)
assert_raises(ValueError, plot_bem, subject='sample',
subjects_dir=subjects_dir, orientation='bad-ori')
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='sagittal', slices=[25, 50])
def check_bem(sbj_id, sbj_dir, raw_info, trans_fname, report):
import os.path as op
import mne
from mne.viz import plot_bem, plot_trans
from mayavi import mlab
import numpy as np
### plot bem surfs to MRI in the 3 different views
fig1 = plot_bem(subject=sbj_id, subjects_dir=sbj_dir, orientation='axial', show=False)
fig2 = plot_bem(subject=sbj_id, subjects_dir=sbj_dir, orientation='sagittal', show=False)
fig3 = plot_bem(subject=sbj_id, subjects_dir=sbj_dir, orientation='coronal', show=False)
report.add_figs_to_section([fig1, fig2, fig3], captions=['axial view', 'sagittal view', 'coronal view'], section='BEM surfaces')
### plot bem surf and source space
bem_fname = op.join(sbj_dir, sbj_id + '/bem/%s-5120-bem-sol.fif' % sbj_id)
surf = mne.read_bem_surfaces(bem_fname, patch_stats=True)
print 'Number of surfaces : %d' % len(surf)
brain_col = (0.95, 0.83, 0.83)
cortex_col = (0.91, 0.89, 0.67)
points = surf[0]['rr']
faces = surf[0]['tris']
fig4 = mlab.figure(size=(600, 600), bgcolor=(0, 0, 0))
mlab.triangular_mesh(points[:, 0], points[:, 1], points[:, 2], faces, color=brain_col, opacity=0.3)
src_fname = op.join(sbj_dir, sbj_id + '/bem/%s-ico-5-src.fif' % sbj_id)
src = mne.read_source_spaces(src_fname)
lh_points = src[0]['rr']
lh_faces = src[0]['tris']
rh_points = src[1]['rr']
rh_faces = src[1]['tris']
mlab.triangular_mesh(lh_points[:, 0], lh_points[:, 1], lh_points[:, 2], lh_faces, color=cortex_col, opacity=0.8)
mlab.triangular_mesh(rh_points[:, 0], rh_points[:, 1], rh_points[:, 2], rh_faces, color=cortex_col, opacity=0.8)
picks = mne.pick_types(raw_info, meg=True, ref_meg=False, eeg=False, stim=False, eog=False, exclude='bads')
### plot sensors
sens_loc = [ raw_info['chs'][picks[i]]['loc'][:3] for i in range(len(picks)) ]
sens_loc = np.array(sens_loc)
mlab.points3d(sens_loc[:, 0], sens_loc[:, 1], sens_loc[:, 2], color=(1, 1, 1), opacity=1, scale_factor=0.005)
report.add_figs_to_section(fig4, captions=['source space'], section='BEM cortex sensors')
fig5 = plot_trans(raw_info, trans_fname, subject=sbj_id, subjects_dir=sbj_dir)
report.add_figs_to_section(fig5, captions=['MEG <-> MRI coregistration quality'], section='MEG <-> MRI')
def test_plot_bem():
"""Test plotting of BEM contours
"""
assert_raises(IOError,
plot_bem,
subject='bad-subject',
subjects_dir=subjects_dir)
assert_raises(ValueError,
plot_bem,
subject='sample',
subjects_dir=subjects_dir,
orientation='bad-ori')
plot_bem(subject='sample',
subjects_dir=subjects_dir,
orientation='sagittal',
slices=[25, 50])
plot_bem(subject='sample',
subjects_dir=subjects_dir,
orientation='coronal',
slices=[25, 50],
brain_surfaces='white')
plot_bem(subject='sample',
subjects_dir=subjects_dir,
orientation='coronal',
slices=[25, 50],
src=src_fname)
def test_plot_bem():
"""Test plotting of BEM contours."""
assert_raises(IOError, plot_bem, subject='bad-subject',
subjects_dir=subjects_dir)
assert_raises(ValueError, plot_bem, subject='sample',
subjects_dir=subjects_dir, orientation='bad-ori')
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='sagittal', slices=[25, 50])
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='coronal', slices=[25, 50],
brain_surfaces='white')
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='coronal', slices=[25, 50], src=src_fname)
"""
============================================================
Plot the head model inside MRI to check anatomical alignment
============================================================
MNE-HCP facilitates source level analysis by supporting linkage between
MRT/anatomy files and the MEG data.
Here, inspect the head model that is used to compute the BEM solution.
"""
# Author: Jona Sassenhagen
# Denis A. Engemann
# License: BSD 3 clause
import os.path as op
from mne.viz import plot_bem
# from mne.bem import make_watershed_bem
storage_dir = op.expanduser('~/mne-hcp-data')
subject = '105923'
subjects_dir = storage_dir + '/hcp-subjects'
###############################################################################
# We assume that all directory structures are set up.
# Checkout :func:`hcp.make_mne_anatomy` to achieve this necessary step.
# See also :ref:`tut_make_anatomy`.
plot_bem(subject=subject, subjects_dir=subjects_dir)
def test_plot_bem():
"""Test plotting of BEM contours
"""
assert_raises(IOError, plot_bem, subject="bad-subject", subjects_dir=subjects_dir)
assert_raises(ValueError, plot_bem, subject="sample", subjects_dir=subjects_dir, orientation="bad-ori")
plot_bem(subject="sample", subjects_dir=subjects_dir, orientation="sagittal", slices=[50, 100])
def test_plot_bem():
"""Test plotting of BEM contours."""
with pytest.raises(IOError, match='MRI file .* not found'):
plot_bem(subject='bad-subject', subjects_dir=subjects_dir)
with pytest.raises(ValueError, match="Invalid value for the 'orientation"):
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='bad-ori')
with pytest.raises(ValueError, match="sorted 1D array"):
plot_bem(subject='sample', subjects_dir=subjects_dir, slices=[0, 500])
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='sagittal', slices=[25, 50])
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='coronal', brain_surfaces='white')
plot_bem(subject='sample', subjects_dir=subjects_dir,
orientation='coronal', slices=[25, 50], src=src_fname)
""" ===================== Plotting BEM Contours ===================== This example displays the BEM surfaces (inner skull, outer skull, outer skin) as yellow contours on top of the T1 MRI anatomical image used for segmentation. This is useful for inspecting the quality of the BEM segmentations which are required for computing the forward solution. """ # Author: Mainak Jas <*****@*****.**> # Alexandre Gramfort <*****@*****.**> # # License: BSD (3-clause) from mne.viz import plot_bem from mne.datasets import sample data_path = sample.data_path() subjects_dir = data_path + '/subjects' plot_bem(subject='sample', subjects_dir=subjects_dir, orientation='axial') plot_bem(subject='sample', subjects_dir=subjects_dir, orientation='sagittal') plot_bem(subject='sample', subjects_dir=subjects_dir, orientation='coronal')
def test_plot_bem():
"""Test plotting of BEM contours."""
with pytest.raises(IOError, match='MRI file .* not found'):
plot_bem(subject='bad-subject', subjects_dir=subjects_dir)
with pytest.raises(ValueError, match="Invalid value for the 'orientation"):
plot_bem(subject='sample',
subjects_dir=subjects_dir,
orientation='bad-ori')
with pytest.raises(ValueError, match="sorted 1D array"):
plot_bem(subject='sample', subjects_dir=subjects_dir, slices=[0, 500])
fig = plot_bem(subject='sample',
subjects_dir=subjects_dir,
orientation='sagittal',
slices=[25, 50])
assert len(fig.axes) == 2
assert len(fig.axes[0].collections) == 3 # 3 BEM surfaces ...
fig = plot_bem(subject='sample',
subjects_dir=subjects_dir,
orientation='coronal',
brain_surfaces='white')
assert len(fig.axes[0].collections) == 5 # 3 BEM surfaces + 2 hemis
fig = plot_bem(subject='sample',
subjects_dir=subjects_dir,
orientation='coronal',
slices=[25, 50],
src=src_fname)
assert len(fig.axes[0].collections) == 4 # 3 BEM surfaces + 1 src contour
with pytest.raises(ValueError, match='MRI coordinates, got head'):
plot_bem(subject='sample', subjects_dir=subjects_dir, src=inv_fname)
surfs = make_bem_model(subject=subject,
subjects_dir=subjects_dir,
conductivity=(.3, ))
write_bem_surfaces(fname=bem_fname, surfs=surfs)
bem = make_bem_solution(surfs)
write_bem_solution(fname=bem_sol_fname, bem=bem)
from mne.viz import plot_bem
if True: # Plot subjects' BEMs and source spaces
figs = list()
for meg_subject, subject in zip(range(1, 21), subjects_id):
if subject in missing_mri + bad_mri:
continue
# Plot BEM
fig = plot_bem(subject=subject, subjects_dir=subjects_dir, show=True)
# plot source space
from mne import read_source_spaces
from surfer import Brain # noqa
from mayavi import mlab # noqa
brain = Brain(subject, 'both', 'inflated')
src = read_source_spaces(
os.path.join(subjects_dir, subject, 'bem',
subject + '-oct-6-src.fif'))
brain = Brain(subject, 'both', 'inflated')
for ii in range(2):
surf = brain.brains[ii]._geo
vertidx = np.where(src[ii]['inuse'])[0]
mlab.points3d(surf.x[vertidx],
surf.y[vertidx],
# single layer
surfs = make_bem_model(subject=subject, subjects_dir=subjects_dir,
conductivity=(.3,))
write_bem_surfaces(fname=bem_fname, surfs=surfs)
bem = make_bem_solution(surfs)
write_bem_solution(fname=bem_sol_fname, bem=bem)
from mne.viz import plot_bem
if True: # Plot subjects' BEMs and source spaces
figs = list()
for meg_subject, subject in zip(range(1, 21), subjects_id):
if subject in missing_mri + bad_mri:
continue
# Plot BEM
fig = plot_bem(subject=subject, subjects_dir=subjects_dir, show=True)
# plot source space
from mne import read_source_spaces
from surfer import Brain # noqa
from mayavi import mlab # noqa
brain = Brain(subject, 'both', 'inflated')
src = read_source_spaces(os.path.join(subjects_dir, subject, 'bem',
subject + '-oct-6-src.fif'))
brain = Brain(subject, 'both', 'inflated')
for ii in range(2):
surf = brain.brains[ii]._geo
vertidx = np.where(src[ii]['inuse'])[0]
mlab.points3d(surf.x[vertidx], surf.y[vertidx],
surf.z[vertidx], color=(1, 1, 0), scale_factor=1.5)
raw_input('next?')
""" ===================== Plotting BEM Contours ===================== This example displays the BEM surfaces (inner skull, outer skull, outer skin) as yellow contours on top of the T1 MRI anatomical image used for segmentation. This is useful for inspecting the quality of the BEM segmentations which are required for computing the forward solution. """ # Author: Mainak Jas <*****@*****.**> # Alexandre Gramfort <*****@*****.**> # # License: BSD (3-clause) from mne.viz import plot_bem from mne.datasets import sample print(__doc__) data_path = sample.data_path() subjects_dir = data_path + '/subjects' plot_bem(subject='sample', subjects_dir=subjects_dir, orientation='axial') plot_bem(subject='sample', subjects_dir=subjects_dir, orientation='sagittal') plot_bem(subject='sample', subjects_dir=subjects_dir, orientation='coronal')
""" ===================== Plotting BEM Contours ===================== This example displays the BEM surfaces (inner skull, outer skull, outer skin) as yellow contours on top of the T1 MRI anatomical image used for segmentation. This is useful for inspecting the quality of the BEM segmentations which are required for computing the forward solution. """ # Author: Mainak Jas <*****@*****.**> # Alexandre Gramfort <*****@*****.**> # # License: BSD (3-clause) from mne.viz import plot_bem from mne.datasets import sample data_path = sample.data_path() subjects_dir = data_path + "/subjects" plot_bem(subject="sample", subjects_dir=subjects_dir, orientation="axial") plot_bem(subject="sample", subjects_dir=subjects_dir, orientation="sagittal") plot_bem(subject="sample", subjects_dir=subjects_dir, orientation="coronal")
