def test_plot_trans():
"""Test plotting of -trans.fif files and MEG sensor layouts
"""
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
preload=False).info
infos = dict(
Neuromag=read_evokeds(evoked_fname)[0].info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
ref_meg = False if system == 'KIT' else True
plot_trans(info, trans_fname, subject='sample', meg_sensors=True,
subjects_dir=subjects_dir, ref_meg=ref_meg)
# KIT ref sensor coil def not defined
assert_raises(RuntimeError, plot_trans, infos['KIT'], None,
meg_sensors=True, ref_meg=True)
info = infos['Neuromag']
assert_raises(ValueError, plot_trans, info, trans_fname,
subject='sample', subjects_dir=subjects_dir,
ch_type='bad-chtype')
assert_raises(TypeError, plot_trans, 'foo', trans_fname,
subject='sample', subjects_dir=subjects_dir)
# no-head version
plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
def test_plot_trans():
"""Test plotting of -trans.fif files
"""
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
plot_trans(evoked.info, trans_fname, subject='sample',
subjects_dir=subjects_dir)
assert_raises(ValueError, plot_trans, evoked.info, trans_fname,
subject='sample', subjects_dir=subjects_dir,
ch_type='bad-chtype')
def test_plot_trans():
"""Test plotting of -trans.fif files
"""
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
plot_trans(evoked.info, trans_fname=trans_fname, subject='sample',
subjects_dir=subjects_dir)
assert_raises(ValueError, plot_trans, evoked.info, trans_fname=trans_fname,
subject='sample', subjects_dir=subjects_dir,
ch_type='bad-chtype')
def test_plot_trans():
"""Test plotting of -trans.fif files and MEG sensor layouts
"""
evoked = read_evokeds(evoked_fname)[0]
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
ref_meg = False if system == 'KIT' else True
plot_trans(info, trans_fname, subject='sample', meg_sensors=True,
subjects_dir=subjects_dir, ref_meg=ref_meg)
# KIT ref sensor coil def is defined
plot_trans(infos['KIT'], None, meg_sensors=True, ref_meg=True)
info = infos['Neuromag']
assert_raises(ValueError, plot_trans, info, trans_fname,
subject='sample', subjects_dir=subjects_dir,
ch_type='bad-chtype')
assert_raises(TypeError, plot_trans, 'foo', trans_fname,
subject='sample', subjects_dir=subjects_dir)
# no-head version
plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
# EEG only with strange options
with warnings.catch_warnings(record=True) as w:
plot_trans(evoked.copy().pick_types(meg=False, eeg=True).info,
trans=trans_fname, meg_sensors=True)
assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])
def test_plot_trans():
"""Test plotting of -trans.fif files
"""
trans_fname = op.join(data_dir, "MEG", "sample", "sample_audvis_raw-trans.fif")
evoked = read_evokeds(evoked_fname, condition="Left Auditory", baseline=(-0.2, 0.0))
plot_trans(evoked.info, trans_fname=trans_fname, subject="sample", subjects_dir=subjects_dir)
assert_raises(
ValueError,
plot_trans,
evoked.info,
trans_fname=trans_fname,
subject="sample",
subjects_dir=subjects_dir,
ch_type="bad-chtype",
)
def test_render_add_sections():
"""Test adding figures/images to section.
"""
tempdir = _TempDir()
import matplotlib.pyplot as plt
report = Report(subjects_dir=subjects_dir)
# Check add_figs_to_section functionality
fig = plt.plot([1, 2], [1, 2])[0].figure
report.add_figs_to_section(figs=fig, # test non-list input
captions=['evoked response'], scale=1.2,
image_format='svg')
assert_raises(ValueError, report.add_figs_to_section, figs=[fig, fig],
captions='H')
assert_raises(ValueError, report.add_figs_to_section, figs=fig,
captions=['foo'], scale=0, image_format='svg')
assert_raises(ValueError, report.add_figs_to_section, figs=fig,
captions=['foo'], scale=1e-10, image_format='svg')
# need to recreate because calls above change size
fig = plt.plot([1, 2], [1, 2])[0].figure
# Check add_images_to_section
img_fname = op.join(tempdir, 'testimage.png')
fig.savefig(img_fname)
report.add_images_to_section(fnames=[img_fname],
captions=['evoked response'])
assert_raises(ValueError, report.add_images_to_section,
fnames=[img_fname, img_fname], captions='H')
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
fig = plot_trans(evoked.info, trans_fname, subject='sample',
subjects_dir=subjects_dir)
report.add_figs_to_section(figs=fig, # test non-list input
captions='random image', scale=1.2)
def test_render_add_sections():
"""Test adding figures/images to section.
"""
tempdir = _TempDir()
import matplotlib.pyplot as plt
report = Report(subjects_dir=subjects_dir)
# Check add_figs_to_section functionality
fig = plt.plot([1, 2], [1, 2])[0].figure
report.add_figs_to_section(figs=fig, # test non-list input
captions=['evoked response'], scale=1.2,
image_format='svg')
assert_raises(ValueError, report.add_figs_to_section, figs=[fig, fig],
captions='H')
assert_raises(ValueError, report.add_figs_to_section, figs=fig,
captions=['foo'], scale=0, image_format='svg')
assert_raises(ValueError, report.add_figs_to_section, figs=fig,
captions=['foo'], scale=1e-10, image_format='svg')
# need to recreate because calls above change size
fig = plt.plot([1, 2], [1, 2])[0].figure
# Check add_images_to_section
img_fname = op.join(tempdir, 'testimage.png')
fig.savefig(img_fname)
report.add_images_to_section(fnames=[img_fname],
captions=['evoked response'])
assert_raises(ValueError, report.add_images_to_section,
fnames=[img_fname, img_fname], captions='H')
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
fig = plot_trans(evoked.info, trans_fname, subject='sample',
subjects_dir=subjects_dir)
report.add_figs_to_section(figs=fig, # test non-list input
captions='random image', scale=1.2)
def test_plot_trans():
"""Test plotting of -trans.fif files and MEG sensor layouts."""
from mayavi import mlab
evoked = read_evokeds(evoked_fname)[0]
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
ref_meg = False if system == 'KIT' else True
plot_trans(info, trans_fname, subject='sample', meg_sensors=True,
subjects_dir=subjects_dir, ref_meg=ref_meg)
mlab.close(all=True)
# KIT ref sensor coil def is defined
plot_trans(infos['KIT'], None, meg_sensors=True, ref_meg=True)
mlab.close(all=True)
info = infos['Neuromag']
assert_raises(ValueError, plot_trans, info, trans_fname,
subject='sample', subjects_dir=subjects_dir,
ch_type='bad-chtype')
assert_raises(TypeError, plot_trans, 'foo', trans_fname,
subject='sample', subjects_dir=subjects_dir)
# no-head version
plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
mlab.close(all=True)
# all coord frames
for coord_frame in ('meg', 'head', 'mri'):
plot_trans(info, meg_sensors=True, dig=True, coord_frame=coord_frame,
trans=trans_fname, subject='sample',
subjects_dir=subjects_dir)
mlab.close(all=True)
# EEG only with strange options
evoked_eeg_ecog = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog.set_channel_types({'EEG 001': 'ecog'})
with warnings.catch_warnings(record=True) as w:
plot_trans(evoked_eeg_ecog.info, subject='sample', trans=trans_fname,
source='outer_skin', meg_sensors=True, skull=True,
eeg_sensors=['original', 'projected'], ecog_sensors=True,
brain='white', head=True, subjects_dir=subjects_dir)
mlab.close(all=True)
assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])
def test_plot_trans():
"""Test plotting of -trans.fif files and MEG sensor layouts."""
from mayavi import mlab
evoked = read_evokeds(evoked_fname)[0]
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
ref_meg = False if system == 'KIT' else True
plot_trans(info, trans_fname, subject='sample', meg_sensors=True,
subjects_dir=subjects_dir, ref_meg=ref_meg)
mlab.close(all=True)
# KIT ref sensor coil def is defined
plot_trans(infos['KIT'], None, meg_sensors=True, ref_meg=True)
mlab.close(all=True)
info = infos['Neuromag']
assert_raises(ValueError, plot_trans, info, trans_fname,
subject='sample', subjects_dir=subjects_dir,
ch_type='bad-chtype')
assert_raises(TypeError, plot_trans, 'foo', trans_fname,
subject='sample', subjects_dir=subjects_dir)
# no-head version
plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
mlab.close(all=True)
# all coord frames
for coord_frame in ('meg', 'head', 'mri'):
plot_trans(info, meg_sensors=True, dig=True, coord_frame=coord_frame,
trans=trans_fname, subject='sample',
subjects_dir=subjects_dir)
mlab.close(all=True)
# EEG only with strange options
with warnings.catch_warnings(record=True) as w:
plot_trans(evoked.copy().pick_types(meg=False, eeg=True).info,
subject='sample', trans=trans_fname, meg_sensors=True,
eeg_sensors=['original', 'projected'],
subjects_dir=subjects_dir)
mlab.close(all=True)
assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])
def test_plot_trans():
"""Test plotting of -trans.fif files
"""
trans_fname = op.join(data_dir, 'MEG', 'sample',
'sample_audvis_raw-trans.fif')
evoked = io.read_evokeds(evoked_fname,
condition='Left Auditory',
baseline=(-0.2, 0.0))
plot_trans(evoked.info,
trans_fname=trans_fname,
subject='sample',
subjects_dir=subjects_dir)
assert_raises(ValueError,
plot_trans,
evoked.info,
trans_fname=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
ch_type='bad-chtype')
def test_render_add_sections():
"""Test adding figures/images to section.
"""
tempdir = _TempDir()
import matplotlib.pyplot as plt
report = Report(subjects_dir=subjects_dir)
# Check add_figs_to_section functionality
fig = plt.plot([1, 2], [1, 2])[0].figure
report.add_figs_to_section(
figs=fig, # test non-list input
captions=['evoked response'],
scale=1.2,
image_format='svg')
assert_raises(ValueError,
report.add_figs_to_section,
figs=[fig, fig],
captions='H')
# Check add_images_to_section
img_fname = op.join(tempdir, 'testimage.png')
fig.savefig(img_fname)
report.add_images_to_section(fnames=[img_fname],
captions=['evoked response'])
assert_raises(ValueError,
report.add_images_to_section,
fnames=[img_fname, img_fname],
captions='H')
# Check deprecation of add_section
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
report.add_section(figs=fig, captions=['evoked response'])
assert_true(w[0].category == DeprecationWarning)
evoked = read_evokeds(evoked_fname,
condition='Left Auditory',
baseline=(-0.2, 0.0))
fig = plot_trans(evoked.info,
trans_fname=trans_fname,
subject='sample',
subjects_dir=subjects_dir)
report.add_figs_to_section(
figs=fig, # test non-list input
captions='random image',
scale=1.2)
def test_snapshot_brain_montage():
info = read_info(evoked_fname)
fig = plot_trans(info, trans=None, subject='sample',
subjects_dir=subjects_dir)
xyz = np.vstack([ich['loc'][:3] for ich in info['chs']])
ch_names = [ich['ch_name'] for ich in info['chs']]
xyz_dict = dict(zip(ch_names, xyz))
xyz_dict[info['chs'][0]['ch_name']] = [1, 2] # Set one ch to only 2 vals
# Make sure wrong types are checked
assert_raises(ValueError, snapshot_brain_montage, fig, xyz)
# All chs must have 3 position values
assert_raises(ValueError, snapshot_brain_montage, fig, xyz_dict)
# Make sure we raise error if the figure has no scene
assert_raises(TypeError, snapshot_brain_montage, fig, info)
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_snapshot_brain_montage():
"""Test snapshot brain montage."""
info = read_info(evoked_fname)
fig = plot_trans(info, trans=None, subject='sample',
subjects_dir=subjects_dir)
xyz = np.vstack([ich['loc'][:3] for ich in info['chs']])
ch_names = [ich['ch_name'] for ich in info['chs']]
xyz_dict = dict(zip(ch_names, xyz))
xyz_dict[info['chs'][0]['ch_name']] = [1, 2] # Set one ch to only 2 vals
# Make sure wrong types are checked
assert_raises(ValueError, snapshot_brain_montage, fig, xyz)
# All chs must have 3 position values
assert_raises(ValueError, snapshot_brain_montage, fig, xyz_dict)
# Make sure we raise error if the figure has no scene
assert_raises(TypeError, snapshot_brain_montage, fig, info)
def test_render_add_sections():
"""Test adding figures/images to section.
"""
from PIL import Image
tempdir = _TempDir()
import matplotlib.pyplot as plt
report = Report(subjects_dir=subjects_dir)
# Check add_figs_to_section functionality
fig = plt.plot([1, 2], [1, 2])[0].figure
report.add_figs_to_section(
figs=fig, captions=["evoked response"], scale=1.2, image_format="svg" # test non-list input
)
assert_raises(ValueError, report.add_figs_to_section, figs=[fig, fig], captions="H")
assert_raises(ValueError, report.add_figs_to_section, figs=fig, captions=["foo"], scale=0, image_format="svg")
assert_raises(ValueError, report.add_figs_to_section, figs=fig, captions=["foo"], scale=1e-10, image_format="svg")
# need to recreate because calls above change size
fig = plt.plot([1, 2], [1, 2])[0].figure
# Check add_images_to_section with png and then gif
img_fname = op.join(tempdir, "testimage.png")
fig.savefig(img_fname)
report.add_images_to_section(fnames=[img_fname], captions=["evoked response"])
im = Image.open(img_fname)
op.join(tempdir, "testimage.gif")
im.save(img_fname) # matplotlib does not support gif
report.add_images_to_section(fnames=[img_fname], captions=["evoked response"])
assert_raises(ValueError, report.add_images_to_section, fnames=[img_fname, img_fname], captions="H")
assert_raises(ValueError, report.add_images_to_section, fnames=["foobar.xxx"], captions="H")
evoked = read_evokeds(evoked_fname, condition="Left Auditory", baseline=(-0.2, 0.0))
fig = plot_trans(evoked.info, trans_fname, subject="sample", subjects_dir=subjects_dir)
report.add_figs_to_section(figs=fig, captions="random image", scale=1.2) # test non-list input
def test_render_add_sections():
"""Test adding figures/images to section.
"""
tempdir = _TempDir()
import matplotlib.pyplot as plt
report = Report(subjects_dir=subjects_dir)
# Check add_figs_to_section functionality
fig = plt.plot([1, 2], [1, 2])[0].figure
report.add_figs_to_section(figs=fig, # test non-list input
captions=['evoked response'], scale=1.2,
image_format='svg')
assert_raises(ValueError, report.add_figs_to_section, figs=[fig, fig],
captions='H')
# Check add_images_to_section
img_fname = op.join(tempdir, 'testimage.png')
fig.savefig(img_fname)
report.add_images_to_section(fnames=[img_fname],
captions=['evoked response'])
assert_raises(ValueError, report.add_images_to_section,
fnames=[img_fname, img_fname], captions='H')
# Check deprecation of add_section
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
report.add_section(figs=fig,
captions=['evoked response'])
assert_true(w[0].category == DeprecationWarning)
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
baseline=(-0.2, 0.0))
fig = plot_trans(evoked.info, trans_fname, subject='sample',
subjects_dir=subjects_dir)
report.add_figs_to_section(figs=fig, # test non-list input
captions='random image', scale=1.2)
"""
=================================
Plotting EEG sensors on the scalp
=================================
In this example, digitized EEG sensor locations are shown on the scalp.
"""
# Author: Eric Larson <*****@*****.**>
#
# License: BSD (3-clause)
import mne
from mne.viz import plot_trans
from mayavi import mlab
print(__doc__)
data_path = mne.datasets.sample.data_path()
subjects_dir = data_path + '/subjects'
trans = mne.read_trans(data_path + '/MEG/sample/sample_audvis_raw-trans.fif')
raw = mne.io.read_raw_fif(data_path + '/MEG/sample/sample_audvis_raw.fif')
fig = plot_trans(raw.info, trans, subject='sample', dig=False,
eeg_sensors=['original', 'projected'],
meg_sensors=[], coord_frame='head', subjects_dir=subjects_dir)
mlab.view(135, 80)
print("Created %s channel positions" % len(ch_names))
###############################################################################
# Now that we have our electrode positions in MRI coordinates, we can create
# our measurement info structure.
info = mne.create_info(ch_names, 1000.0, "ecog", montage=mon)
###############################################################################
# We can then plot the locations of our electrodes on our subject's brain.
#
# .. note:: These are not real electrodes for this subject, so they
# do not align to the cortical surface perfectly.
subjects_dir = mne.datasets.sample.data_path() + "/subjects"
fig = plot_trans(info, trans=None, subject="sample", subjects_dir=subjects_dir)
mlab.view(200, 70)
###############################################################################
# Sometimes it is useful to make a scatterplot for the current figure view.
# This is best accomplished with matplotlib. We can capture an image of the
# current mayavi view, along with the xy position of each electrode, with the
# `snapshot_brain_montage` function.
# We'll once again plot the surface, then take a snapshot.
fig = plot_trans(info, trans=None, subject="sample", subjects_dir=subjects_dir)
mlab.view(200, 70)
xy, im = snapshot_brain_montage(fig, mon)
# Convert from a dictionary to array to plot
xy_pts = np.vstack(xy[ch] for ch in info["ch_names"])
raw_fname = paths('sss', subject=meg_subject, block=1)
trans_fname = paths('trans', subject=meg_subject)
# XXX for one subject the HPI were adequately triangulated before the
# first block
if subject == 'ps120458':
raw_fname.split('1-sss.fif')[0] + '2-sss.fif'
# Manual coregistration
print(subject, meg_subject)
coregistration(subject=subject,
subjects_dir=subjects_dir,
inst=raw_fname)
# Plot
info = read_info(raw_fname)
plot_trans(info,
trans_fname,
subject=subject,
dig=True,
meg_sensors=True)
def _copy_from_fsaverage(subject, subjects_dir, overwrite=False):
"""Copy fsaverage files for subjects with missing MRI"""
for this_dir in ['bem', 'surf']:
bem_dir = os.path.join(subjects_dir, subject, this_dir)
if not os.path.exists(bem_dir):
os.makedirs(bem_dir)
f_from = os.path.join(subjects_dir, 'fsaverage', 'bem',
'fsaverage-5120-bem.fif')
f_to = os.path.join(subjects_dir, subject, 'bem',
'%s-5120-bem.fif' % subject)
if overwrite or not os.path.exists(f_to):
=========================================
Plotting head in helmet from a trans file
=========================================
In this example, the head is shown in the
MEG helmet along with the EEG electrodes in MRI
coordinate system. This allows assessing the
MEG <-> MRI coregistration quality.
"""
# Author: Mainak Jas <*****@*****.**>
#
# License: BSD (3-clause)
from mne import read_evokeds
from mne.datasets import sample
from mne.viz import plot_trans
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
evoked_fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
trans_fname = data_path + '/MEG/sample/sample_audvis_raw-trans.fif'
condition = 'Left Auditory'
evoked = read_evokeds(evoked_fname, condition=condition, baseline=(-0.2, 0.0))
plot_trans(evoked.info, trans_fname, subject='sample', dig=True,
meg_sensors=True, subjects_dir=subjects_dir)
for meg_subject, subject in zip(range(1, 21), subjects_id):
if subject in missing_mri + bad_mri:
continue
raw_fname = paths('sss', subject=meg_subject, block=1)
trans_fname = paths('trans', subject=meg_subject)
# XXX for one subject the HPI were adequately triangulated before the
# first block
if subject == 'ps120458':
raw_fname.split('1-sss.fif')[0] + '2-sss.fif'
# Manual coregistration
print(subject, meg_subject)
coregistration(subject=subject, subjects_dir=subjects_dir,
inst=raw_fname)
# Plot
info = read_info(raw_fname)
plot_trans(info, trans_fname, subject=subject, dig=True,
meg_sensors=True)
def _copy_from_fsaverage(subject, subjects_dir, overwrite=False):
"""Copy fsaverage files for subjects with missing MRI"""
for this_dir in ['bem', 'surf']:
bem_dir = os.path.join(subjects_dir, subject, this_dir)
if not os.path.exists(bem_dir):
os.makedirs(bem_dir)
f_from = os.path.join(subjects_dir, 'fsaverage', 'bem',
'fsaverage-5120-bem.fif')
f_to = os.path.join(subjects_dir, subject, 'bem',
'%s-5120-bem.fif' % subject)
if overwrite or not os.path.exists(f_to):
copyfile(f_from, f_to)
surf_files = [
elec = mat['elec']
dig_ch_pos = dict(zip(ch_names, elec))
mon = mne.channels.DigMontage(dig_ch_pos=dig_ch_pos)
info = mne.create_info(ch_names, 1000., 'ecog', montage=mon)
print('Created %s channel positions' % len(ch_names))
###############################################################################
# Project 3D electrodes to a 2D snapshot
# --------------------------------------
#
# Because we have the 3D location of each electrode, we can use the
# :func:`mne.viz.snapshot_brain_montage` function to return a 2D image along
# with the electrode positions on that image. We use this in conjunction with
# :func:`mne.viz.plot_trans`, which visualizes electrode positions.
fig = plot_trans(info, trans=None, subject='sample', subjects_dir=subjects_dir)
mlab.view(200, 70)
xy, im = snapshot_brain_montage(fig, mon)
# Convert from a dictionary to array to plot
xy_pts = np.vstack(xy[ch] for ch in info['ch_names'])
# Define an arbitrary "activity" pattern for viz
activity = np.linspace(100, 200, xy_pts.shape[0])
# This allows us to use matplotlib to create arbitrary 2d scatterplots
fig2, ax = plt.subplots(figsize=(10, 10))
ax.imshow(im)
ax.scatter(*xy_pts.T, c=activity, s=200, cmap='coolwarm')
ax.set_axis_off()
# fig2.savefig('./brain.png', bbox_inches='tight') # For ClickableImage
Plotting head in helmet from a trans file
=========================================
In this example, the head is shown in the
MEG helmet along with the EEG electrodes in MRI
coordinate system. This allows assessing the
MEG <-> MRI coregistration quality.
"""
# Author: Mainak Jas <*****@*****.**>
#
# License: BSD (3-clause)
from mne import read_evokeds
from mne.datasets import sample
from mne.viz import plot_trans
data_path = sample.data_path()
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
evoked_fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
trans_fname = data_path + '/MEG/sample/sample_audvis_raw-trans.fif'
condition = 'Left Auditory'
evoked = read_evokeds(evoked_fname, condition=condition, baseline=(-0.2, 0.0))
plot_trans(evoked.info,
trans_fname=trans_fname,
subject='sample',
subjects_dir=subjects_dir)
print('Created %s channel positions' % len(ch_names))
###############################################################################
# Now that we have our electrode positions in MRI coordinates, we can create
# our measurement info structure.
info = mne.create_info(ch_names, 1000., 'ecog', montage=mon)
###############################################################################
# We can then plot the locations of our electrodes on our subject's brain.
#
# .. note:: These are not real electrodes for this subject, so they
# do not align to the cortical surface perfectly.
subjects_dir = mne.datasets.sample.data_path() + '/subjects'
fig = plot_trans(info, trans=None, subject='sample', subjects_dir=subjects_dir)
mlab.view(200, 70)
###############################################################################
# Sometimes it is useful to make a scatterplot for the current figure view.
# This is best accomplished with matplotlib. We can capture an image of the
# current mayavi view, along with the xy position of each electrode, with the
# `snapshot_brain_montage` function.
# We'll once again plot the surface, then take a snapshot.
fig = plot_trans(info, trans=None, subject='sample', subjects_dir=subjects_dir)
mlab.view(200, 70)
xy, im = snapshot_brain_montage(fig, mon)
# Convert from a dictionary to array to plot
xy_pts = np.vstack(xy[ch] for ch in info['ch_names'])
def test_plot_trans():
"""Test plotting of -trans.fif files and MEG sensor layouts."""
mlab = _import_mlab()
evoked = read_evokeds(evoked_fname)[0]
sample_src = read_source_spaces(src_fname)
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname,
config_fname,
hs_fname,
convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
ref_meg = False if system == 'KIT' else True
plot_trans(info,
trans_fname,
subject='sample',
meg_sensors=True,
subjects_dir=subjects_dir,
ref_meg=ref_meg)
mlab.close(all=True)
# KIT ref sensor coil def is defined
plot_trans(infos['KIT'], None, meg_sensors=True, ref_meg=True)
mlab.close(all=True)
info = infos['Neuromag']
assert_raises(ValueError,
plot_trans,
info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
ch_type='bad-chtype')
assert_raises(TypeError,
plot_trans,
'foo',
trans_fname,
subject='sample',
subjects_dir=subjects_dir)
assert_raises(TypeError,
plot_trans,
info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
src='foo')
assert_raises(ValueError,
plot_trans,
info,
trans_fname,
subject='fsaverage',
subjects_dir=subjects_dir,
src=sample_src)
sample_src.plot(subjects_dir=subjects_dir)
mlab.close(all=True)
# no-head version
plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
mlab.close(all=True)
# all coord frames
for coord_frame in ('meg', 'head', 'mri'):
plot_trans(info,
meg_sensors=True,
dig=True,
coord_frame=coord_frame,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir)
mlab.close(all=True)
# EEG only with strange options
evoked_eeg_ecog = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog.set_channel_types({'EEG 001': 'ecog'})
with warnings.catch_warnings(record=True) as w:
plot_trans(evoked_eeg_ecog.info,
subject='sample',
trans=trans_fname,
source='outer_skin',
meg_sensors=True,
skull=True,
eeg_sensors=['original', 'projected'],
ecog_sensors=True,
brain='white',
head=True,
subjects_dir=subjects_dir)
mlab.close(all=True)
assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])
import os.path as op
from mayavi import mlab
import mne
from mne.io import Raw, read_raw_ctf, read_raw_bti, read_raw_kit
from mne.datasets import sample, spm_face
from mne.viz import plot_trans
print(__doc__)
bti_path = op.abspath(op.dirname(mne.__file__)) + '/io/bti/tests/data/'
kit_path = op.abspath(op.dirname(mne.__file__)) + '/io/kit/tests/data/'
raws = dict(
Neuromag=Raw(sample.data_path() + '/MEG/sample/sample_audvis_raw.fif'),
CTF_275=read_raw_ctf(spm_face.data_path() +
'/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds'),
Magnes_3600wh=read_raw_bti(bti_path + 'test_pdf_linux',
bti_path + 'test_config_linux',
bti_path + 'test_hs_linux'),
KIT=read_raw_kit(kit_path + 'test.sqd'),
)
for system, raw in raws.items():
# We don't have coil definitions for KIT refs, so exclude them
ref_meg = False if system == 'KIT' else True
fig = plot_trans(raw.info, trans=None, dig=False, eeg_sensors=False,
meg_sensors=True, coord_frame='meg', ref_meg=ref_meg)
mlab.title(system)
import mne
import os
# SETUP PATHS AND PREPARE RAW DATA
hostname = socket.gethostname()
if hostname == "wintermute":
data_path = "/home/mje/mnt/caa/scratch/"
else:
data_path = "/projects/MINDLAB2015_MEG-CorticalAlphaAttention/scratch/"
# CHANGE DIR TO SAVE FILES THE RIGTH PLACE
os.chdir(data_path)
subjects_dir = data_path + "fs_subjects_dir/"
save_folder = data_path + "filter_ica_data/"
maxfiltered_folder = data_path + "maxfiltered_data/"
epochs_folder = data_path + "epoched_data/"
tf_folder = data_path + "tf_data/"
mne_folder = data_path + "minimum_norm/"
subject = "0025"
trans_fname = mne_folder + "%s-trans.fif" % subject
epochs = mne.read_epochs(epochs_folder +
"%s_filtered_ica_mc_tsss-epo.fif" % subject,
preload=False)
plot_trans(epochs.info, trans_fname, subject=subject, dig=True,
subjects_dir=subjects_dir)
def test_plot_trans():
"""Test plotting of -trans.fif files and MEG sensor layouts."""
# generate fiducials file for testing
tempdir = _TempDir()
fiducials_path = op.join(tempdir, 'fiducials.fif')
fid = [{'coord_frame': 5, 'ident': 1, 'kind': 1,
'r': [-0.08061612, -0.02908875, -0.04131077]},
{'coord_frame': 5, 'ident': 2, 'kind': 1,
'r': [0.00146763, 0.08506715, -0.03483611]},
{'coord_frame': 5, 'ident': 3, 'kind': 1,
'r': [0.08436285, -0.02850276, -0.04127743]}]
write_dig(fiducials_path, fid, 5)
mlab = _import_mlab()
evoked = read_evokeds(evoked_fname)[0]
sample_src = read_source_spaces(src_fname)
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
ref_meg = False if system == 'KIT' else True
plot_trans(info, trans_fname, subject='sample', meg_sensors=True,
subjects_dir=subjects_dir, ref_meg=ref_meg)
mlab.close(all=True)
# KIT ref sensor coil def is defined
plot_trans(infos['KIT'], None, meg_sensors=True, ref_meg=True)
mlab.close(all=True)
info = infos['Neuromag']
assert_raises(TypeError, plot_trans, 'foo', trans_fname,
subject='sample', subjects_dir=subjects_dir)
assert_raises(TypeError, plot_trans, info, trans_fname,
subject='sample', subjects_dir=subjects_dir, src='foo')
assert_raises(ValueError, plot_trans, info, trans_fname,
subject='fsaverage', subjects_dir=subjects_dir,
src=sample_src)
sample_src.plot(subjects_dir=subjects_dir)
mlab.close(all=True)
# no-head version
plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
mlab.close(all=True)
# all coord frames
for coord_frame in ('meg', 'head', 'mri'):
plot_trans(info, meg_sensors=True, dig=True, coord_frame=coord_frame,
trans=trans_fname, subject='sample',
mri_fiducials=fiducials_path, subjects_dir=subjects_dir)
mlab.close(all=True)
# EEG only with strange options
evoked_eeg_ecog = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog.set_channel_types({'EEG 001': 'ecog'})
with warnings.catch_warnings(record=True) as w:
plot_trans(evoked_eeg_ecog.info, subject='sample', trans=trans_fname,
source='outer_skin', meg_sensors=True, skull=True,
eeg_sensors=['original', 'projected'], ecog_sensors=True,
brain='white', head=True, subjects_dir=subjects_dir)
mlab.close(all=True)
assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])
def test_plot_trans():
"""Test plotting of -trans.fif files and MEG sensor layouts."""
# generate fiducials file for testing
tempdir = _TempDir()
fiducials_path = op.join(tempdir, 'fiducials.fif')
fid = [{
'coord_frame': 5,
'ident': 1,
'kind': 1,
'r': [-0.08061612, -0.02908875, -0.04131077]
}, {
'coord_frame': 5,
'ident': 2,
'kind': 1,
'r': [0.00146763, 0.08506715, -0.03483611]
}, {
'coord_frame': 5,
'ident': 3,
'kind': 1,
'r': [0.08436285, -0.02850276, -0.04127743]
}]
write_dig(fiducials_path, fid, 5)
mlab = _import_mlab()
evoked = read_evokeds(evoked_fname)[0]
sample_src = read_source_spaces(src_fname)
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname,
config_fname,
hs_fname,
convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
ref_meg = False if system == 'KIT' else True
plot_trans(info,
trans_fname,
subject='sample',
meg_sensors=True,
subjects_dir=subjects_dir,
ref_meg=ref_meg)
mlab.close(all=True)
# KIT ref sensor coil def is defined
plot_trans(infos['KIT'], None, meg_sensors=True, ref_meg=True)
mlab.close(all=True)
info = infos['Neuromag']
assert_raises(ValueError,
plot_trans,
info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
ch_type='bad-chtype')
assert_raises(TypeError,
plot_trans,
'foo',
trans_fname,
subject='sample',
subjects_dir=subjects_dir)
assert_raises(TypeError,
plot_trans,
info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
src='foo')
assert_raises(ValueError,
plot_trans,
info,
trans_fname,
subject='fsaverage',
subjects_dir=subjects_dir,
src=sample_src)
sample_src.plot(subjects_dir=subjects_dir)
mlab.close(all=True)
# no-head version
plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
mlab.close(all=True)
# all coord frames
for coord_frame in ('meg', 'head', 'mri'):
plot_trans(info,
meg_sensors=True,
dig=True,
coord_frame=coord_frame,
trans=trans_fname,
subject='sample',
mri_fiducials=fiducials_path,
subjects_dir=subjects_dir)
mlab.close(all=True)
# EEG only with strange options
evoked_eeg_ecog = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog.set_channel_types({'EEG 001': 'ecog'})
with warnings.catch_warnings(record=True) as w:
plot_trans(evoked_eeg_ecog.info,
subject='sample',
trans=trans_fname,
source='outer_skin',
meg_sensors=True,
skull=True,
eeg_sensors=['original', 'projected'],
ecog_sensors=True,
brain='white',
head=True,
subjects_dir=subjects_dir)
mlab.close(all=True)
assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])
=========================================
In this example, the head is shown in the
MEG helmet along with the EEG electrodes in MRI
coordinate system. This allows assessing the
MEG <-> MRI coregistration quality.
"""
# Author: Mainak Jas <*****@*****.**>
#
# License: BSD (3-clause)
from mne import read_evokeds
from mne.datasets import sample
from mne.viz import plot_trans
print(__doc__)
data_path = sample.data_path()
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
evoked_fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
trans_fname = data_path + '/MEG/sample/sample_audvis_raw-trans.fif'
condition = 'Left Auditory'
evoked = read_evokeds(evoked_fname, condition=condition, baseline=(-0.2, 0.0))
plot_trans(evoked.info, trans_fname, subject='sample',
subjects_dir=subjects_dir)
def test_plot_alignment():
"""Test plotting of -trans.fif files and MEG sensor layouts."""
# generate fiducials file for testing
tempdir = _TempDir()
fiducials_path = op.join(tempdir, 'fiducials.fif')
fid = [{'coord_frame': 5, 'ident': 1, 'kind': 1,
'r': [-0.08061612, -0.02908875, -0.04131077]},
{'coord_frame': 5, 'ident': 2, 'kind': 1,
'r': [0.00146763, 0.08506715, -0.03483611]},
{'coord_frame': 5, 'ident': 3, 'kind': 1,
'r': [0.08436285, -0.02850276, -0.04127743]}]
write_dig(fiducials_path, fid, 5)
mlab = _import_mlab()
evoked = read_evokeds(evoked_fname)[0]
sample_src = read_source_spaces(src_fname)
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
meg = ['helmet', 'sensors']
if system == 'KIT':
meg.append('ref')
plot_alignment(info, trans_fname, subject='sample',
subjects_dir=subjects_dir, meg=meg)
mlab.close(all=True)
# KIT ref sensor coil def is defined
plot_trans(infos['KIT'], None, meg_sensors=True, ref_meg=True)
mlab.close(all=True)
info = infos['Neuromag']
assert_raises(TypeError, plot_alignment, 'foo', trans_fname,
subject='sample', subjects_dir=subjects_dir)
assert_raises(TypeError, plot_alignment, info, trans_fname,
subject='sample', subjects_dir=subjects_dir, src='foo')
assert_raises(ValueError, plot_alignment, info, trans_fname,
subject='fsaverage', subjects_dir=subjects_dir,
src=sample_src)
sample_src.plot(subjects_dir=subjects_dir, head=True, skull=True,
brain='white')
mlab.close(all=True)
# no-head version
plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
mlab.close(all=True)
# all coord frames
for coord_frame in ('meg', 'head', 'mri'):
plot_alignment(info, meg=['helmet', 'sensors'], dig=True,
coord_frame=coord_frame, trans=trans_fname,
subject='sample', mri_fiducials=fiducials_path,
subjects_dir=subjects_dir, src=sample_src)
mlab.close(all=True)
# EEG only with strange options
evoked_eeg_ecog = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog.set_channel_types({'EEG 001': 'ecog'})
with warnings.catch_warnings(record=True) as w:
plot_alignment(evoked_eeg_ecog.info, subject='sample',
trans=trans_fname, subjects_dir=subjects_dir,
surfaces=['white', 'outer_skin', 'outer_skull'],
meg=['helmet', 'sensors'],
eeg=['original', 'projected'], ecog=True)
mlab.close(all=True)
assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])
sphere = make_sphere_model(info=evoked.info, r0='auto', head_radius='auto')
bem_sol = read_bem_solution(op.join(subjects_dir, 'sample', 'bem',
'sample-1280-1280-1280-bem-sol.fif'))
bem_surfs = read_bem_surfaces(op.join(subjects_dir, 'sample', 'bem',
'sample-1280-1280-1280-bem.fif'))
sample_src[0]['coord_frame'] = 4 # hack for coverage
plot_alignment(info, trans_fname, subject='sample', meg='helmet',
subjects_dir=subjects_dir, eeg='projected', bem=sphere,
surfaces=['head', 'brain', 'inner_skull', 'outer_skull'],
src=sample_src)
plot_alignment(info, trans_fname, subject='sample', meg=[],
subjects_dir=subjects_dir, bem=bem_sol, eeg=True,
surfaces=['head', 'inflated', 'outer_skull', 'inner_skull'])
plot_alignment(info, trans_fname, subject='sample',
meg=True, subjects_dir=subjects_dir,
surfaces=['head', 'inner_skull'], bem=bem_surfs)
sphere = make_sphere_model('auto', None, evoked.info) # one layer
plot_alignment(info, trans_fname, subject='sample', meg=False,
coord_frame='mri', subjects_dir=subjects_dir,
surfaces=['brain'], bem=sphere)
# one layer bem with skull surfaces:
assert_raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['brain', 'head', 'inner_skull'], bem=sphere)
# wrong eeg value:
assert_raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir, eeg='foo')
# wrong meg value:
assert_raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir, meg='bar')
# multiple brain surfaces:
assert_raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['white', 'pial'])
def test_plot_alignment():
"""Test plotting of -trans.fif files and MEG sensor layouts."""
# generate fiducials file for testing
tempdir = _TempDir()
fiducials_path = op.join(tempdir, 'fiducials.fif')
fid = [{
'coord_frame': 5,
'ident': 1,
'kind': 1,
'r': [-0.08061612, -0.02908875, -0.04131077]
}, {
'coord_frame': 5,
'ident': 2,
'kind': 1,
'r': [0.00146763, 0.08506715, -0.03483611]
}, {
'coord_frame': 5,
'ident': 3,
'kind': 1,
'r': [0.08436285, -0.02850276, -0.04127743]
}]
write_dig(fiducials_path, fid, 5)
mlab = _import_mlab()
evoked = read_evokeds(evoked_fname)[0]
sample_src = read_source_spaces(src_fname)
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname,
config_fname,
hs_fname,
convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
meg = ['helmet', 'sensors']
if system == 'KIT':
meg.append('ref')
plot_alignment(info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
meg=meg)
mlab.close(all=True)
# KIT ref sensor coil def is defined
plot_trans(infos['KIT'], None, meg_sensors=True, ref_meg=True)
mlab.close(all=True)
info = infos['Neuromag']
assert_raises(TypeError,
plot_alignment,
'foo',
trans_fname,
subject='sample',
subjects_dir=subjects_dir)
assert_raises(TypeError,
plot_alignment,
info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
src='foo')
assert_raises(ValueError,
plot_alignment,
info,
trans_fname,
subject='fsaverage',
subjects_dir=subjects_dir,
src=sample_src)
sample_src.plot(subjects_dir=subjects_dir,
head=True,
skull=True,
brain='white')
mlab.close(all=True)
# no-head version
plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
mlab.close(all=True)
# all coord frames
for coord_frame in ('meg', 'head', 'mri'):
plot_alignment(info,
meg=['helmet', 'sensors'],
dig=True,
coord_frame=coord_frame,
trans=trans_fname,
subject='sample',
mri_fiducials=fiducials_path,
subjects_dir=subjects_dir,
src=sample_src)
mlab.close(all=True)
# EEG only with strange options
evoked_eeg_ecog = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog.set_channel_types({'EEG 001': 'ecog'})
with warnings.catch_warnings(record=True) as w:
plot_alignment(evoked_eeg_ecog.info,
subject='sample',
trans=trans_fname,
subjects_dir=subjects_dir,
surfaces=['white', 'outer_skin', 'outer_skull'],
meg=['helmet', 'sensors'],
eeg=['original', 'projected'],
ecog=True)
mlab.close(all=True)
assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])
sphere = make_sphere_model(info=evoked.info, r0='auto', head_radius='auto')
bem_sol = read_bem_solution(
op.join(subjects_dir, 'sample', 'bem',
'sample-1280-1280-1280-bem-sol.fif'))
bem_surfs = read_bem_surfaces(
op.join(subjects_dir, 'sample', 'bem',
'sample-1280-1280-1280-bem.fif'))
sample_src[0]['coord_frame'] = 4 # hack for coverage
plot_alignment(info,
trans_fname,
subject='sample',
meg='helmet',
subjects_dir=subjects_dir,
eeg='projected',
bem=sphere,
surfaces=['head', 'brain', 'inner_skull', 'outer_skull'],
src=sample_src)
plot_alignment(info,
trans_fname,
subject='sample',
meg=[],
subjects_dir=subjects_dir,
bem=bem_sol,
eeg=True,
surfaces=['head', 'inflated', 'outer_skull', 'inner_skull'])
plot_alignment(info,
trans_fname,
subject='sample',
meg=True,
subjects_dir=subjects_dir,
surfaces=['head', 'inner_skull'],
bem=bem_surfs)
sphere = make_sphere_model('auto', None, evoked.info) # one layer
plot_alignment(info,
trans_fname,
subject='sample',
meg=False,
coord_frame='mri',
subjects_dir=subjects_dir,
surfaces=['brain'],
bem=sphere)
# one layer bem with skull surfaces:
assert_raises(ValueError,
plot_alignment,
info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['brain', 'head', 'inner_skull'],
bem=sphere)
# wrong eeg value:
assert_raises(ValueError,
plot_alignment,
info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
eeg='foo')
# wrong meg value:
assert_raises(ValueError,
plot_alignment,
info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
meg='bar')
# multiple brain surfaces:
assert_raises(ValueError,
plot_alignment,
info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['white', 'pial'])
Plotting EEG sensors on the scalp
=================================
In this example, digitized EEG sensor locations are shown on the scalp.
"""
# Author: Eric Larson <*****@*****.**>
#
# License: BSD (3-clause)
import mne
from mne.viz import plot_trans
from mayavi import mlab
print(__doc__)
data_path = mne.datasets.sample.data_path()
subjects_dir = data_path + "/subjects"
trans = mne.read_trans(data_path + "/MEG/sample/sample_audvis_raw-trans.fif")
raw = mne.io.read_raw_fif(data_path + "/MEG/sample/sample_audvis_raw.fif")
fig = plot_trans(
raw.info,
trans,
subject="sample",
dig=False,
eeg_sensors=["original", "projected"],
meg_sensors=[],
coord_frame="head",
subjects_dir=subjects_dir,
)
mlab.view(135, 80)
raws = dict(Neuromag=read_raw_fif(sample.data_path() +
'/MEG/sample/sample_audvis_raw.fif'),
CTF_275=read_raw_ctf(spm_face.data_path() +
'/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds'),
Magnes_3600wh=read_raw_bti(op.join(bti_path, 'test_pdf_linux'),
op.join(bti_path, 'test_config_linux'),
op.join(bti_path, 'test_hs_linux')),
KIT=read_raw_kit(op.join(kit_path, 'test.sqd')),
Artemis123=read_raw_artemis123(
op.join(testing.data_path(), 'ARTEMIS123',
'Artemis_Data_2016-11-03-15h-58m_test.bin')))
for system, raw in raws.items():
# We don't have coil definitions for KIT refs, so exclude them
ref_meg = False if system == 'KIT' else True
fig = plot_trans(raw.info,
trans=None,
dig=False,
eeg_sensors=False,
meg_sensors=True,
coord_frame='meg',
ref_meg=ref_meg)
text = mlab.title(system)
text.x_position = 0.5
text.y_position = 0.95
text.property.vertical_justification = 'top'
text.property.justification = 'center'
text.actor.text_scale_mode = 'none'
text.property.bold = True
mlab.draw(fig)
