def plot_spline(subject_name, subjects_dir, xgt, ygt, zgt, electrodes,
mlabViewX, mlabViewY):
#make info file
ch_names = [str(x) for x in np.arange(64)]
dig_ch_pos = dict(zip(ch_names, electrodes / 1000))
mon = mne.channels.DigMontage(dig_ch_pos=dig_ch_pos)
info = mne.create_info(ch_names, 1000., ch_types='ecog', montage=mon)
fig = mlab.figure('TPS', size=(800, 700))
plot_alignment(info,
subject=subject_name,
subjects_dir=subjects_dir,
surfaces=['pial'],
ecog=False,
fig=fig)
pts = mlab.points3d(xgt, ygt, zgt, scale_mode='none', scale_factor=0.0003)
# Create and visualize the mesh
mesh = mlab.pipeline.delaunay2d(pts)
surf = mlab.pipeline.surface(mesh,
representation='surface',
colormap='rainbow')
mlab.points3d(electrodes[:, 0] / 1000,
electrodes[:, 1] / 1000,
electrodes[:, 2] / 1000,
color=(1, 0, 0),
scale_factor=0.002)
mlab.view(mlabViewX, mlabViewY)
def save_images(subject_, montage_fname_):
montage = mne.channels.read_dig_fif(montage_fname_)
info = mne.create_info(ch_names=montage.ch_names,
ch_types=['eeg'] * len(montage.ch_names),
sfreq=100.0)
info.set_montage(montage)
trans = mne.channels.compute_native_head_t(montage)
fig_ = plot_alignment(info,
eeg='projected',
show_axes=False,
trans=trans,
surfaces={"head": 1.0},
coord_frame='mri',
subject=subject_)
fig_.plotter.off_screen = True
set_3d_view(figure=fig_, azimuth=135, elevation=80, distance=0.4)
fig_.plotter.screenshot(subject_ + "_1.png")
set_3d_view(figure=fig_, azimuth=45, elevation=80, distance=0.4)
fig_.plotter.screenshot(subject_ + "_2.png")
set_3d_view(figure=fig_, azimuth=270, elevation=80, distance=0.4)
fig_.plotter.screenshot(subject_ + "_3.png")
fig_ = plot_alignment(info,
eeg=['original', 'projected'],
show_axes=True,
trans=trans,
surfaces="head",
coord_frame='mri',
dig=True,
subject=subject_)
fig_.plotter.off_screen = True
set_3d_view(figure=fig_,
azimuth=135,
elevation=80,
distance=0.5,
focalpoint=np.linalg.inv(trans["trans"])[:3, 3])
fig_.plotter.screenshot(subject_ + "_clear_1.png")
set_3d_view(figure=fig_,
azimuth=45,
elevation=80,
distance=0.5,
focalpoint=np.linalg.inv(trans["trans"])[:3, 3])
fig_.plotter.screenshot(subject_ + "_clear_2.png")
set_3d_view(figure=fig_,
azimuth=270,
elevation=80,
distance=0.5,
focalpoint=np.linalg.inv(trans["trans"])[:3, 3])
fig_.plotter.screenshot(subject_ + "_clear_3.png")
def test_render_add_sections(renderer, tmp_path):
"""Test adding figures/images to section."""
tempdir = str(tmp_path)
report = Report(subjects_dir=subjects_dir)
# Check add_figure functionality
fig = plt.plot([1, 2], [1, 2])[0].figure
report.add_figure(fig=fig, title='evoked response', image_format='svg')
assert 'caption' not in report._content[-1].html
report.add_figure(fig=fig, title='evoked with caption', caption='descr')
assert 'caption' in report._content[-1].html
# Check add_image with png
img_fname = op.join(tempdir, 'testimage.png')
fig.savefig(img_fname)
report.add_image(image=img_fname, title='evoked response')
with pytest.raises(FileNotFoundError, match='No such file or directory'):
report.add_image(image='foobar.xxx', title='H')
evoked = read_evokeds(evoked_fname,
condition='Left Auditory',
baseline=(-0.2, 0.0))
fig = plot_alignment(evoked.info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir)
report.add_figure(fig=fig, title='random image')
assert (repr(report))
fname = op.join(str(tmp_path), 'test.html')
report.save(fname, open_browser=False)
assert len(report) == 4
def test_snapshot_brain_montage(backends_3d):
"""Test snapshot brain montage."""
from mne.viz import get_3d_backend
if get_3d_backend() == 'pyvista':
pytest.skip("This feature is not available yet on PyVista")
info = read_info(evoked_fname)
fig = plot_alignment(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
pytest.raises(TypeError, snapshot_brain_montage, fig, xyz)
# All chs must have 3 position values
pytest.raises(ValueError, snapshot_brain_montage, fig, xyz_dict)
# Make sure we raise error if the figure has no scene
pytest.raises(ValueError, snapshot_brain_montage, None, info)
def test_plot_alignment_meg(renderer, system):
"""Test plotting of MEG sensors + helmet."""
if system == 'Neuromag':
this_info = read_info(evoked_fname)
elif system == 'CTF':
this_info = read_raw_ctf(ctf_fname).info
elif system == 'BTi':
this_info = read_raw_bti(pdf_fname,
config_fname,
hs_fname,
convert=True,
preload=False).info
else:
assert system == 'KIT'
this_info = read_raw_kit(sqd_fname).info
meg = ['helmet', 'sensors']
if system == 'KIT':
meg.append('ref')
fig = plot_alignment(this_info,
read_trans(trans_fname),
subject='sample',
subjects_dir=subjects_dir,
meg=meg,
eeg=False)
# count the number of objects: should be n_meg_ch + 1 (helmet) + 1 (head)
use_info = pick_info(
this_info,
pick_types(this_info,
meg=True,
eeg=False,
ref_meg='ref' in meg,
exclude=()))
n_actors = use_info['nchan'] + 2
_assert_n_actors(fig, renderer, n_actors)
def test_plot_alignment_surf(renderer):
"""Test plotting of a surface."""
info = read_info(evoked_fname)
fig = plot_alignment(
info, read_trans(trans_fname), subject='sample',
subjects_dir=subjects_dir, meg=False, eeg=False, dig=False,
surfaces=['white', 'head'])
_assert_n_actors(fig, renderer, 3) # left and right hemis plus head
def brain_plot(subject_name, subjects_dir, electrodes, mlabViewX, mlabViewY,
textBool):
#make info file
ch_names = [str(x) for x in np.arange(64)]
dig_ch_pos = dict(zip(ch_names, electrodes / 1000))
mon = mne.channels.DigMontage(dig_ch_pos=dig_ch_pos)
info = mne.create_info(ch_names, 1000., ch_types='ecog', montage=mon)
#plot electrodes as spheres or as numbers
if textBool:
fig = mlab.figure('brain text', size=(800, 700))
#plot subject's brain
plot_alignment(info,
subject=subject_name,
subjects_dir=subjects_dir,
surfaces=['pial'],
ecog=False,
fig=fig)
fig.scene.disable_render = True
for i, x in enumerate(electrodes):
mlab.text3d(x[0] / 1000,
x[1] / 1000,
x[2] / 1000,
str(i + 1),
scale=0.001)
fig.scene.disable_render = False # Super duper trick
else:
fig = mlab.figure('brain spheres', size=(800, 700))
#plot subject's brain
plot_alignment(info,
subject=subject_name,
subjects_dir=subjects_dir,
surfaces=['pial'],
ecog=False,
fig=fig)
mlab.points3d(electrodes[:, 0] / 1000,
electrodes[:, 1] / 1000,
electrodes[:, 2] / 1000,
color=(1, 0, 0),
scale_factor=0.002)
#chouse point of view
mlab.view(mlabViewX, mlabViewY)
def test_plot_alignment_fnirs(renderer, tmp_path):
"""Test fNIRS plotting."""
# Here we use subjects_dir=tmp_path, since no surfaces should actually
# be loaded!
# fNIRS (default is pairs)
info = read_raw_nirx(nirx_fname).info
assert info['nchan'] == 26
kwargs = dict(trans='fsaverage', subject='fsaverage', surfaces=(),
verbose=True, subjects_dir=tmp_path)
with catch_logging() as log:
fig = plot_alignment(info, **kwargs)
log = log.getvalue()
assert f'fnirs_cw_amplitude: {info["nchan"]}' in log
_assert_n_actors(fig, renderer, info['nchan'])
fig = plot_alignment(
info, fnirs=['channels', 'sources', 'detectors'], **kwargs)
_assert_n_actors(fig, renderer, 3)
def plot_gamma_motor(subject_name, subjects_dir, dir_n, elec_up, mlabViewX,
mlabViewY, saveDataBool):
#make info file
ch_names = [str(x) for x in np.arange(64)]
dig_ch_pos = dict(zip(ch_names, elec_up / 1000))
mon = mne.channels.DigMontage(dig_ch_pos=dig_ch_pos)
info = mne.create_info(ch_names, 1000., ch_types='ecog', montage=mon)
name = listdir(dir_n)
for nam in name:
if nam != '.DS_Store':
gama_t = loadmat(dir_n + nam)
res_re = gama_t['res']
s = np.mean(res_re[:, 15:, 0], axis=1) #gamma
s = np.interp(s, (s.min(), s.max()), (0, 1))
fig = mlab.figure('gamma_motor_' + nam, size=(800, 700))
plot_alignment(info,
subject=subject_name,
subjects_dir=subjects_dir,
surfaces=['pial'],
ecog=False,
fig=fig)
obj = mlab.points3d(elec_up[:, 0] / 1000,
elec_up[:, 1] / 1000,
elec_up[:, 2] / 1000,
s,
colormap='rainbow',
scale_mode='none',
scale_factor=0.002)
mlab.colorbar(object=obj,
title='gamma',
orientation='vertical',
nb_labels=8,
nb_colors=None,
label_fmt=None)
mlab.title(nam[:-4], size=0.2, height=0.015)
#chouse point of view
mlab.view(mlabViewX, mlabViewY)
if saveDataBool:
mlab.savefig(nam[:-4] + '.png')
mlab.savefig(nam[:-4] + '.x3d')
def plot_registration(info, trans, subject, subjects_dir):
fig = plot_alignment(info,
trans,
subject=subject,
dig=True,
meg=True,
subjects_dir=subjects_dir,
coord_frame='head')
set_3d_view(figure=fig, azimuth=135, elevation=80)
mlab.savefig('/home/senthilp/Desktop/coreg.jpg')
Image(filename='/home/senthilp/Desktop/coreg.jpg', width=500)
mlab.show()
def plot_accuracy(subject_name, subjects_dir, dir_n, elec_up, mlabViewX,
mlabViewY, saveDataBool):
#make info file
ch_names = [str(x) for x in np.arange(64)]
dig_ch_pos = dict(zip(ch_names, elec_up / 1000))
mon = mne.channels.DigMontage(dig_ch_pos=dig_ch_pos)
info = mne.create_info(ch_names, 1000., ch_types='ecog', montage=mon)
name = listdir(dir_n)
for nam in name:
if nam != '.DS_Store':
s = np.loadtxt(dir_n + nam)
s = np.nan_to_num(s[:, 1])
fig = mlab.figure('accuracy_' + nam, size=(800, 700))
plot_alignment(info,
subject=subject_name,
subjects_dir=subjects_dir,
surfaces=['pial'],
ecog=False,
fig=fig)
obj = mlab.points3d(elec_up[:, 0] / 1000,
elec_up[:, 1] / 1000,
elec_up[:, 2] / 1000,
s,
colormap='rainbow',
scale_mode='none',
scale_factor=0.002)
mlab.colorbar(object=obj,
title='accuracy',
orientation='vertical',
nb_labels=8,
nb_colors=None,
label_fmt=None)
mlab.title(nam[:-4], size=0.2, height=0.015)
#chouse point of view
mlab.view(mlabViewX, mlabViewY)
if saveDataBool:
mlab.savefig(nam[:-4] + '.png')
mlab.savefig(nam[:-4] + '.x3d')
def plot_alpha_touch(subject_name, subjects_dir, file_n, elec_up, mlabViewX,
mlabViewY, saveDataBool):
#make info file
ch_names = [str(x) for x in np.arange(64)]
dig_ch_pos = dict(zip(ch_names, elec_up / 1000))
mon = mne.channels.DigMontage(dig_ch_pos=dig_ch_pos)
info = mne.create_info(ch_names, 1000., ch_types='ecog', montage=mon)
data_t = loadmat(file_n)
res_re = data_t['res']
for i in range(5):
s = np.mean(res_re[:, :5, i], axis=1) #alpha
s = np.interp(s, (s.min(), s.max()), (0, 1))
fig = mlab.figure('alpha_touch_' + str(i), size=(800, 700))
plot_alignment(info,
subject=subject_name,
subjects_dir=subjects_dir,
surfaces=['pial'],
ecog=False,
fig=fig)
obj = mlab.points3d(elec_up[:, 0] / 1000,
elec_up[:, 1] / 1000,
elec_up[:, 2] / 1000,
s,
colormap='rainbow',
scale_mode='none',
scale_factor=0.002)
mlab.colorbar(object=obj,
title='alpha',
orientation='vertical',
nb_labels=8,
nb_colors=None,
label_fmt=None)
mlab.title('touch ' + str(i + 1), size=0.2, height=0.015)
#chouse point of view
mlab.view(mlabViewX, mlabViewY)
if saveDataBool:
mlab.savefig('alpha_touch_' + str(i + 1) + '.png')
mlab.savefig('alpha_touch_' + str(i + 1) + '.x3d')
def test_render_add_sections(renderer, tmpdir):
"""Test adding figures/images to section."""
tempdir = str(tmpdir)
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')
pytest.raises(ValueError, report.add_figs_to_section, figs=[fig, fig],
captions='H')
pytest.raises(ValueError, report.add_figs_to_section, figs=fig,
captions=['foo'], scale=0, image_format='svg')
pytest.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
img_fname = op.join(tempdir, 'testimage.png')
fig.savefig(img_fname)
report.add_images_to_section(fnames=[img_fname],
captions=['evoked response'])
report.add_images_to_section(fnames=[img_fname],
captions=['evoked response'])
pytest.raises(ValueError, report.add_images_to_section,
fnames=[img_fname, img_fname], captions='H')
pytest.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_alignment(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)
assert (repr(report))
fname = op.join(str(tmpdir), 'test.html')
report.save(fname, open_browser=False)
with open(fname, 'r') as fid:
html = fid.read()
assert html.count('<li class="report_custom"') == 8 # several
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, # 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 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_alignment(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)
assert_true(repr(report))
def test_snapshot_brain_montage():
"""Test snapshot brain montage."""
info = read_info(evoked_fname)
fig = plot_alignment(
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
pytest.raises(TypeError, snapshot_brain_montage, fig, xyz)
# All chs must have 3 position values
pytest.raises(ValueError, snapshot_brain_montage, fig, xyz_dict)
# Make sure we raise error if the figure has no scene
pytest.raises(TypeError, snapshot_brain_montage, fig, info)
def test_snapshot_brain_montage(renderer):
"""Test snapshot brain montage."""
info = read_info(evoked_fname)
fig = plot_alignment(
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
pytest.raises(TypeError, snapshot_brain_montage, fig, xyz)
# All chs must have 3 position values
pytest.raises(ValueError, snapshot_brain_montage, fig, xyz_dict)
# Make sure we raise error if the figure has no scene
pytest.raises(ValueError, snapshot_brain_montage, None, info)
def plot_locs(seeg_loc, info, study_path):
from mne.viz import plot_alignment
from mayavi import mlab
subjects_dir = op.join(study_path, 'freesurfer_subjects')
subject = seeg_loc['subject'][0]
fig = plot_alignment(info,
subject=seeg_loc['subject'][0],
subjects_dir=subjects_dir,
surfaces=['pial'],
meg=False,
coord_frame='head')
mlab.view(200, 70)
mlab.show()
file_nodes = op.join(study_path, 'physio_data', subject, 'chan_info',
'%s.node' % subject)
make_bnw_nodes(file_nodes,
coords=seeg_loc[['x', 'y', 'z']],
colors=1.,
sizes=1.)
def test_snapshot_brain_montage(backends_3d):
"""Test snapshot brain montage."""
from mne.viz import get_3d_backend
if get_3d_backend() == 'pyvista':
pytest.skip("This feature is not available yet on PyVista")
info = read_info(evoked_fname)
fig = plot_alignment(
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
pytest.raises(TypeError, snapshot_brain_montage, fig, xyz)
# All chs must have 3 position values
pytest.raises(ValueError, snapshot_brain_montage, fig, xyz_dict)
# Make sure we raise error if the figure has no scene
pytest.raises(ValueError, snapshot_brain_montage, None, info)
def test_plot_alignment(tmpdir):
"""Test plotting of -trans.fif files and MEG sensor layouts."""
# generate fiducials file for testing
tempdir = str(tmpdir)
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)
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
mlab.close(all=True)
info = infos['Neuromag']
pytest.raises(TypeError, plot_alignment, 'foo', trans_fname,
subject='sample', subjects_dir=subjects_dir)
pytest.raises(TypeError, plot_alignment, info, trans_fname,
subject='sample', subjects_dir=subjects_dir, src='foo')
pytest.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
mlab.close(all=True)
# all coord frames
pytest.raises(ValueError, plot_alignment, info)
plot_alignment(info, surfaces=[])
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_seeg = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog_seeg.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog_seeg.set_channel_types({'EEG 001': 'ecog',
'EEG 002': 'seeg'})
with pytest.warns(RuntimeWarning, match='Cannot plot MEG'):
plot_alignment(evoked_eeg_ecog_seeg.info, subject='sample',
trans=trans_fname, subjects_dir=subjects_dir,
surfaces=['white', 'outer_skin', 'outer_skull'],
meg=['helmet', 'sensors'],
eeg=['original', 'projected'], ecog=True, seeg=True)
mlab.close(all=True)
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, subject='sample', eeg='projected',
meg='helmet', bem=sphere, dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull',
'outer_skin'])
plot_alignment(info, trans_fname, subject='sample', meg='helmet',
subjects_dir=subjects_dir, eeg='projected', bem=sphere,
surfaces=['head', 'brain'], src=sample_src)
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
plot_alignment(info, trans_fname, subject='sample', meg=[],
subjects_dir=subjects_dir, bem=bem_sol, eeg=True,
surfaces=['head', 'inflated', 'outer_skull', 'inner_skull'])
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
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', 'auto', evoked.info)
src = setup_volume_source_space(sphere=sphere)
plot_alignment(info, eeg='projected', meg='helmet', bem=sphere,
src=src, dig=True, surfaces=['brain', 'inner_skull',
'outer_skull', 'outer_skin'])
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, show_axes=True)
# 3D coil with no defined draw (ConvexHull)
info_cube = pick_info(info, [0])
info['dig'] = None
info_cube['chs'][0]['coil_type'] = 9999
with pytest.raises(RuntimeError, match='coil definition not found'):
plot_alignment(info_cube, meg='sensors', surfaces=())
coil_def_fname = op.join(tempdir, 'temp')
with open(coil_def_fname, 'w') as fid:
fid.write(coil_3d)
with use_coil_def(coil_def_fname):
plot_alignment(info_cube, meg='sensors', surfaces=(), dig=True)
# one layer bem with skull surfaces:
pytest.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:
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir, eeg='foo')
# wrong meg value:
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir, meg='bar')
# multiple brain surfaces:
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['white', 'pial'])
pytest.raises(TypeError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=[1])
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['foo'])
mlab.close(all=True)
# Finally, make evoked from the one epoch
evoked = epochs.average()
###############################################################################
# Explore the electrodes on a template brain
# ------------------------------------------
#
# Our electrodes are shown after being morphed to fsaverage brain so we'll use
# this fsaverage brain to plot the locations of our electrodes. We'll use
# :func:`~mne.viz.snapshot_brain_montage` to save the plot as image data
# (along with xy positions of each electrode in the image), so that later
# we can plot frequency band power on top of it.
fig = plot_alignment(raw.info,
subject='fsaverage',
subjects_dir=subjects_dir,
surfaces=['pial'],
coord_frame='mri')
az, el, focalpoint = 160, -70, [0.067, -0.040, 0.018]
mne.viz.set_3d_view(fig, azimuth=az, elevation=el, focalpoint=focalpoint)
xy, im = snapshot_brain_montage(fig, raw.info)
###############################################################################
# Compute frequency features of the data
# --------------------------------------
#
# Next, we'll compute the signal power in the gamma (30-90 Hz) band,
# downsampling the result to 10 Hz (to save time).
sfreq = 10
def test_plot_alignment(tmpdir, backends_3d):
"""Test plotting of -trans.fif files and MEG sensor layouts."""
backend_name = get_3d_backend()
# generate fiducials file for testing
tempdir = str(tmpdir)
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)
if backend_name == 'mayavi':
mlab = _import_mlab()
evoked = read_evokeds(evoked_fname)[0]
sample_src = read_source_spaces(src_fname)
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)
if backend_name == 'mayavi':
mlab.close(all=True)
# KIT ref sensor coil def is defined
if backend_name == 'mayavi':
mlab.close(all=True)
info = infos['Neuromag']
pytest.raises(TypeError,
plot_alignment,
'foo',
trans_fname,
subject='sample',
subjects_dir=subjects_dir)
pytest.raises(OSError,
plot_alignment,
info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
src='foo')
pytest.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')
if backend_name == 'mayavi':
mlab.close(all=True)
# no-head version
if backend_name == 'mayavi':
mlab.close(all=True)
# all coord frames
pytest.raises(ValueError, plot_alignment, info)
plot_alignment(info, surfaces=[])
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=src_fname)
if backend_name == 'mayavi':
mlab.close(all=True)
# EEG only with strange options
evoked_eeg_ecog_seeg = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog_seeg.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog_seeg.set_channel_types({
'EEG 001': 'ecog',
'EEG 002': 'seeg'
})
with pytest.warns(RuntimeWarning, match='Cannot plot MEG'):
plot_alignment(evoked_eeg_ecog_seeg.info,
subject='sample',
trans=trans_fname,
subjects_dir=subjects_dir,
surfaces=['white', 'outer_skin', 'outer_skull'],
meg=['helmet', 'sensors'],
eeg=['original', 'projected'],
ecog=True,
seeg=True)
if backend_name == 'mayavi':
mlab.close(all=True)
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,
subject='sample',
eeg='projected',
meg='helmet',
bem=sphere,
dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull', 'outer_skin'])
plot_alignment(info,
trans_fname,
subject='sample',
meg='helmet',
subjects_dir=subjects_dir,
eeg='projected',
bem=sphere,
surfaces=['head', 'brain'],
src=sample_src)
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
plot_alignment(info,
trans_fname,
subject='sample',
meg=[],
subjects_dir=subjects_dir,
bem=bem_sol,
eeg=True,
surfaces=['head', 'inflated', 'outer_skull', 'inner_skull'])
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
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', 'auto', evoked.info)
src = setup_volume_source_space(sphere=sphere)
plot_alignment(
info,
eeg='projected',
meg='helmet',
bem=sphere,
src=src,
dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull', 'outer_skin'])
sphere = make_sphere_model('auto', None, evoked.info) # one layer
# no info is permitted
fig = plot_alignment(trans=trans_fname,
subject='sample',
meg=False,
coord_frame='mri',
subjects_dir=subjects_dir,
surfaces=['brain'],
bem=sphere,
show_axes=True)
if backend_name == 'mayavi':
import mayavi # noqa: F401 analysis:ignore
assert isinstance(fig, mayavi.core.scene.Scene)
# 3D coil with no defined draw (ConvexHull)
info_cube = pick_info(info, [0])
info['dig'] = None
info_cube['chs'][0]['coil_type'] = 9999
with pytest.raises(RuntimeError, match='coil definition not found'):
plot_alignment(info_cube, meg='sensors', surfaces=())
coil_def_fname = op.join(tempdir, 'temp')
with open(coil_def_fname, 'w') as fid:
fid.write(coil_3d)
with use_coil_def(coil_def_fname):
plot_alignment(info_cube, meg='sensors', surfaces=(), dig=True)
# one layer bem with skull surfaces:
pytest.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:
pytest.raises(ValueError,
plot_alignment,
info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
eeg='foo')
# wrong meg value:
pytest.raises(ValueError,
plot_alignment,
info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
meg='bar')
# multiple brain surfaces:
pytest.raises(ValueError,
plot_alignment,
info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['white', 'pial'])
pytest.raises(TypeError,
plot_alignment,
info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=[1])
pytest.raises(ValueError,
plot_alignment,
info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['foo'])
if backend_name == 'mayavi':
mlab.close(all=True)
# evoked data.
raw.set_eeg_reference(projection=True)
events = mne.find_events(raw)
epochs = mne.Epochs(raw, events)
cov = mne.compute_covariance(epochs, tmax=0.)
evoked = epochs['1'].average() # trigger 1 in auditory/left
evoked.plot_joint()
##############################################################################
# Getting a source estimate
# -------------------------
# New we have all of the components we need to compute a forward solution,
# but first we should sanity check that everything is well aligned:
plot_alignment(evoked.info, trans=trans, show_axes=True, surfaces='head-dense',
subject='sample', subjects_dir=subjects_dir)
##############################################################################
# Now we can actually compute the forward:
fwd = mne.make_forward_solution(
evoked.info, trans=trans, src=fname_src, bem=fname_bem, verbose=True)
##############################################################################
# Finally let's compute the inverse and apply it:
inv = mne.minimum_norm.make_inverse_operator(
evoked.info, fwd, cov, verbose=True)
stc = mne.minimum_norm.apply_inverse(evoked, inv)
stc.plot(subjects_dir=subjects_dir, initial_time=0.1)
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_alignment(info, subject='sample', subjects_dir=subjects_dir,
surfaces=['pial'])
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_alignment(info, subject='sample', subjects_dir=subjects_dir,
surfaces='pial')
mlab.view(200, 70)
xy, im = snapshot_brain_montage(fig, mon)
# Convert from a dictionary to array to plot
def plot_nirs_source_detector(data,
info=None,
radius=0.001,
trans=None,
subject=None,
subjects_dir=None,
surfaces='head',
coord_frame='head',
meg=None,
eeg='original',
fwd=None,
dig=False,
ecog=True,
src=None,
mri_fiducials=False,
bem=None,
seeg=True,
fnirs=False,
show_axes=False,
fig=None,
cmap=None,
interaction='trackball',
verbose=None):
"""
3D visualisation of fNIRS response magnitude.
This function plots the response amplitude for each channel.
Each channel is represented by a line between the source and detector,
the color of the line reflects the response magnitude.
Parameters
----------
data : array
Array of values to be plotted between source and detectors.
One value should be specified per channel in the same order
as `info.chs`.
info : dict | None
The measurement info.
If None (default), no sensor information will be shown.
radius : numbers
Tube radius for connecting links.
%(trans)s
subject : str | None
The subject name corresponding to FreeSurfer environment
variable SUBJECT. Can be omitted if ``src`` is provided.
%(subjects_dir)s
surfaces : str | list
Surfaces to plot. Supported values:
* scalp: one of 'head', 'outer_skin' (alias for 'head'),
'head-dense', or 'seghead' (alias for 'head-dense')
* skull: 'outer_skull', 'inner_skull', 'brain' (alias for
'inner_skull')
* brain: one of 'pial', 'white', 'inflated', or 'brain'
(alias for 'pial').
Defaults to 'head'.
.. note:: For single layer BEMs it is recommended to use 'brain'.
coord_frame : str
Coordinate frame to use, 'head', 'meg', or 'mri'.
meg : str | list | bool | None
Can be "helmet", "sensors" or "ref" to show the MEG helmet, sensors or
reference sensors respectively, or a combination like
``('helmet', 'sensors')`` (same as None, default). True translates to
``('helmet', 'sensors', 'ref')``.
eeg : bool | str | list
String options are:
- "original" (default; equivalent to ``True``)
Shows EEG sensors using their digitized locations (after
transformation to the chosen ``coord_frame``)
- "projected"
The EEG locations projected onto the scalp, as is done in forward
modeling
Can also be a list of these options, or an empty list (``[]``,
equivalent of ``False``).
fwd : instance of Forward
The forward solution. If present, the orientations of the dipoles
present in the forward solution are displayed.
dig : bool | 'fiducials'
If True, plot the digitization points; 'fiducials' to plot fiducial
points only.
ecog : bool
If True (default), show ECoG sensors.
src : instance of SourceSpaces | None
If not None, also plot the source space points.
mri_fiducials : bool | str
Plot MRI fiducials (default False). If ``True``, look for a file with
the canonical name (``bem/{subject}-fiducials.fif``). If ``str`` it
should provide the full path to the fiducials file.
bem : list of dict | instance of ConductorModel | None
Can be either the BEM surfaces (list of dict), a BEM solution or a
sphere model. If None, we first try loading
`'$SUBJECTS_DIR/$SUBJECT/bem/$SUBJECT-$SOURCE.fif'`, and then look for
`'$SUBJECT*$SOURCE.fif'` in the same directory. For `'outer_skin'`,
the subjects bem and bem/flash folders are searched. Defaults to None.
seeg : bool
If True (default), show sEEG electrodes.
fnirs : bool
If True (default), show fNIRS electrodes.
show_axes : bool
If True (default False), coordinate frame axis indicators will be
shown:
* head in pink.
* MRI in gray (if ``trans is not None``).
* MEG in blue (if MEG sensors are present).
.. versionadded:: 0.16
fig : mayavi.mlab.Figure | None
Mayavi Scene in which to plot the alignment.
If ``None``, creates a new 600x600 pixel figure with black background.
.. versionadded:: 0.16
cmap : str
Colormap to be used.
interaction : str
Can be "trackball" (default) or "terrain", i.e. a turntable-style
camera.
.. versionadded:: 0.16
%(verbose)s
Returns
-------
fig : Figure
The 3D figure.
Notes
-----
For more information see :func:`mne.viz.plot_alignment`.
.. versionadded:: 0.15
"""
# Determine range of values for creating colormap
vmin = np.min(data)
vmax = np.max(data)
# If no colormap is specified choose depending on range of data
if cmap is None:
if (vmin >= 0) & (vmax >= 0):
# For positive only data use magma
cmap = 'Oranges'
else:
# Otherwise use blue to red and ensure zero sits at white
vmin = -1. * np.max(np.abs(data))
vmax = np.max(np.abs(data))
cmap = 'RdBu_r'
if isinstance(radius, (int, float)):
radius = np.ones(len(info['chs'])) * radius
# Plot requested alignment
fig = plot_alignment(info=info,
trans=trans,
subject=subject,
subjects_dir=subjects_dir,
surfaces=surfaces,
coord_frame=coord_frame,
meg=meg,
eeg=eeg,
fwd=fwd,
dig=dig,
ecog=ecog,
src=src,
mri_fiducials=mri_fiducials,
bem=bem,
seeg=seeg,
fnirs=fnirs,
show_axes=show_axes,
fig=fig,
interaction=interaction,
verbose=verbose)
from mne.viz.backends.renderer import _get_renderer
renderer = _get_renderer(fig)
# Overlay channels between source and detectors
for idx, ch in enumerate(info['chs']):
locs = ch['loc']
renderer.tube(origin=[np.array([locs[3], locs[4], locs[5]])],
destination=[np.array([locs[6], locs[7], locs[8]])],
scalars=np.array([[1.0, 1.0]]) * data[idx],
radius=radius[idx],
colormap=cmap,
vmin=vmin,
vmax=vmax)
t = renderer.tube(origin=[np.array([0, 0, 0])],
destination=[np.array([0, 0, 0.001])],
scalars=np.array([[vmin, vmax]]),
radius=0.0001,
colormap=cmap,
vmin=vmin,
vmax=vmax)
renderer.scalarbar(t)
return fig
raws = {
'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_2017-04-14-10h-38m-59s_Phantom_1k_HPI_1s.bin')),
}
for system, raw in sorted(raws.items()):
meg = ['helmet', 'sensors']
# We don't have coil definitions for KIT refs, so exclude them
if system != 'KIT':
meg.append('ref')
fig = plot_alignment(raw.info,
eeg=False,
meg=('helmet', 'sensors'),
coord_frame='meg',
show_axes=True,
verbose=True)
set_3d_title(figure=fig, title=system)
import mne
from mne.channels.montage import get_builtin_montages
from mne.datasets import fetch_fsaverage
from mne.viz import plot_alignment
subjects_dir = op.dirname(fetch_fsaverage())
###############################################################################
# check all montages
#
for current_montage in get_builtin_montages():
montage = mne.channels.read_montage(current_montage,
unit='auto',
transform=False)
info = mne.create_info(ch_names=montage.ch_names,
sfreq=1,
ch_types='eeg',
montage=montage)
fig = plot_alignment(info, trans=None,
subject='fsaverage',
subjects_dir=subjects_dir,
eeg=['projected'],
)
mlab.view(135, 80)
mlab.title(montage.kind, figure=fig)
def test_plot_alignment(tmpdir, renderer):
"""Test plotting of -trans.fif files and MEG sensor layouts."""
# generate fiducials file for testing
tempdir = str(tmpdir)
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)
renderer._close_all()
evoked = read_evokeds(evoked_fname)[0]
sample_src = read_source_spaces(src_fname)
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')
fig = plot_alignment(info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
meg=meg)
rend = renderer._Renderer(fig=fig)
rend.close()
# KIT ref sensor coil def is defined
renderer._close_all()
info = infos['Neuromag']
pytest.raises(TypeError,
plot_alignment,
'foo',
trans_fname,
subject='sample',
subjects_dir=subjects_dir)
pytest.raises(OSError,
plot_alignment,
info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
src='foo')
pytest.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')
renderer._close_all()
# no-head version
renderer._close_all()
# all coord frames
pytest.raises(ValueError, plot_alignment, info)
plot_alignment(info, surfaces=[])
for coord_frame in ('meg', 'head', 'mri'):
fig = 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=src_fname)
renderer._close_all()
# EEG only with strange options
evoked_eeg_ecog_seeg = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog_seeg.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog_seeg.set_channel_types({
'EEG 001': 'ecog',
'EEG 002': 'seeg'
})
with pytest.warns(RuntimeWarning, match='Cannot plot MEG'):
plot_alignment(evoked_eeg_ecog_seeg.info,
subject='sample',
trans=trans_fname,
subjects_dir=subjects_dir,
surfaces=['white', 'outer_skin', 'outer_skull'],
meg=['helmet', 'sensors'],
eeg=['original', 'projected'],
ecog=True,
seeg=True)
renderer._close_all()
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,
subject='sample',
eeg='projected',
meg='helmet',
bem=sphere,
dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull', 'outer_skin'])
plot_alignment(info,
trans_fname,
subject='sample',
meg='helmet',
subjects_dir=subjects_dir,
eeg='projected',
bem=sphere,
surfaces=['head', 'brain'],
src=sample_src)
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
plot_alignment(info,
trans_fname,
subject='sample',
meg=[],
subjects_dir=subjects_dir,
bem=bem_sol,
eeg=True,
surfaces=['head', 'inflated', 'outer_skull', 'inner_skull'])
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
plot_alignment(info,
trans_fname,
subject='sample',
meg=True,
subjects_dir=subjects_dir,
surfaces=['head', 'inner_skull'],
bem=bem_surfs)
# single-layer BEM can still plot head surface
assert bem_surfs[-1]['id'] == FIFF.FIFFV_BEM_SURF_ID_BRAIN
bem_sol_homog = read_bem_solution(
op.join(subjects_dir, 'sample', 'bem', 'sample-1280-bem-sol.fif'))
for use_bem in (bem_surfs[-1:], bem_sol_homog):
with catch_logging() as log:
plot_alignment(info,
trans_fname,
subject='sample',
meg=True,
subjects_dir=subjects_dir,
surfaces=['head', 'inner_skull'],
bem=use_bem,
verbose=True)
log = log.getvalue()
assert 'not find the surface for head in the provided BEM model' in log
# sphere model
sphere = make_sphere_model('auto', 'auto', evoked.info)
src = setup_volume_source_space(sphere=sphere)
plot_alignment(
info,
eeg='projected',
meg='helmet',
bem=sphere,
src=src,
dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull', 'outer_skin'])
sphere = make_sphere_model('auto', None, evoked.info) # one layer
# no info is permitted
fig = plot_alignment(trans=trans_fname,
subject='sample',
meg=False,
coord_frame='mri',
subjects_dir=subjects_dir,
surfaces=['brain'],
bem=sphere,
show_axes=True)
renderer._close_all()
if renderer.get_3d_backend() == 'mayavi':
import mayavi # noqa: F401 analysis:ignore
assert isinstance(fig, mayavi.core.scene.Scene)
# 3D coil with no defined draw (ConvexHull)
info_cube = pick_info(info, [0])
info['dig'] = None
info_cube['chs'][0]['coil_type'] = 9999
with pytest.raises(RuntimeError, match='coil definition not found'):
plot_alignment(info_cube, meg='sensors', surfaces=())
coil_def_fname = op.join(tempdir, 'temp')
with open(coil_def_fname, 'w') as fid:
fid.write(coil_3d)
with use_coil_def(coil_def_fname):
plot_alignment(info_cube, meg='sensors', surfaces=(), dig=True)
# one layer bem with skull surfaces:
with pytest.raises(ValueError, match='sphere conductor model must have'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['brain', 'head', 'inner_skull'],
bem=sphere)
# wrong eeg value:
with pytest.raises(ValueError, match='eeg must only contain'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
eeg='foo')
# wrong meg value:
with pytest.raises(ValueError, match='meg must only contain'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
meg='bar')
# multiple brain surfaces:
with pytest.raises(ValueError, match='Only one brain surface can be plot'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['white', 'pial'])
with pytest.raises(TypeError, match='all entries in surfaces must be'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=[1])
with pytest.raises(ValueError, match='Unknown surface type'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['foo'])
fwd_fname = op.join(data_dir, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif')
fwd = read_forward_solution(fwd_fname)
plot_alignment(subject='sample',
subjects_dir=subjects_dir,
trans=trans_fname,
fwd=fwd,
surfaces='white',
coord_frame='head')
fwd = convert_forward_solution(fwd, force_fixed=True)
plot_alignment(subject='sample',
subjects_dir=subjects_dir,
trans=trans_fname,
fwd=fwd,
surfaces='white',
coord_frame='head')
renderer._close_all()
###############################################################################
# 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_alignment(info,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['pial'])
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_alignment(info,
subject='sample',
subjects_dir=subjects_dir,
surfaces='pial')
mlab.view(200, 70)
import mne
from mne.io import read_raw_fif, read_raw_ctf, read_raw_bti, read_raw_kit
from mne.io import read_raw_artemis123
from mne.datasets import sample, spm_face, testing
from mne.viz import plot_alignment, set_3d_title
print(__doc__)
###############################################################################
# Neuromag
# --------
kwargs = dict(eeg=False, coord_frame='meg', show_axes=True, verbose=True)
raw = read_raw_fif(sample.data_path() + '/MEG/sample/sample_audvis_raw.fif')
fig = plot_alignment(raw.info, meg=('helmet', 'sensors'), **kwargs)
set_3d_title(figure=fig, title='Neuromag')
###############################################################################
# CTF
# ---
raw = read_raw_ctf(spm_face.data_path() +
'/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds')
fig = plot_alignment(raw.info, meg=('helmet', 'sensors', 'ref'), **kwargs)
set_3d_title(figure=fig, title='CTF 275')
###############################################################################
# BTi
# ---
elec = mat['elec'] # electrode coordinates in meters
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_alignment`, which visualizes electrode positions.
fig = plot_alignment(info, subject='sample', subjects_dir=subjects_dir,
surfaces=['pial'], meg=False)
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 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_alignment
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_alignment(raw.info, trans, subject='sample', dig=False,
eeg=['original', 'projected'], meg=[],
coord_frame='head', subjects_dir=subjects_dir)
mlab.view(135, 80)
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
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
mlab.close(all=True)
# all coord frames
assert_raises(ValueError, plot_alignment, info)
plot_alignment(info, surfaces=[])
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, subject='sample', eeg='projected',
meg='helmet', bem=sphere, dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull',
'outer_skin'])
plot_alignment(info, trans_fname, subject='sample', meg='helmet',
subjects_dir=subjects_dir, eeg='projected', bem=sphere,
surfaces=['head', 'brain'], 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', 'auto', evoked.info)
src = setup_volume_source_space(sphere=sphere)
plot_alignment(info, eeg='projected', meg='helmet', bem=sphere,
src=src, dig=True, surfaces=['brain', 'inner_skull',
'outer_skull', 'outer_skin'])
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, show_axes=True)
# 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'])
assert_raises(TypeError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=[1])
assert_raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['foo'])
mlab.close(all=True)
"""
=================================
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_alignment
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_alignment(raw.info,
trans,
subject='sample',
dig=False,
eeg=['original', 'projected'],
meg=[],
coord_frame='head',
subjects_dir=subjects_dir)
mlab.view(135, 80)
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_2017-04-14-10h-38m-59s_Phantom_1k_HPI_1s.bin')))
for system, raw in raws.items():
meg = ['helmet', 'sensors']
# We don't have coil definitions for KIT refs, so exclude them
if system != 'KIT':
meg.append('ref')
fig = plot_alignment(raw.info,
trans=None,
dig=False,
eeg=False,
surfaces=[],
meg=meg,
coord_frame='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)
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
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
mlab.close(all=True)
# all coord frames
assert_raises(ValueError, plot_alignment, info)
plot_alignment(info, surfaces=[])
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, subject='sample', eeg='projected',
meg='helmet', bem=sphere, dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull',
'outer_skin'])
plot_alignment(info, trans_fname, subject='sample', meg='helmet',
subjects_dir=subjects_dir, eeg='projected', bem=sphere,
surfaces=['head', 'brain'], 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', 'auto', evoked.info)
src = setup_volume_source_space(sphere=sphere)
plot_alignment(info, eeg='projected', meg='helmet', bem=sphere,
src=src, dig=True, surfaces=['brain', 'inner_skull',
'outer_skull', 'outer_skin'])
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, show_axes=True)
# 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'])
assert_raises(TypeError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=[1])
assert_raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['foo'])
mlab.close(all=True)
coord_frame='head')
info = mne.create_info(ch_names, 1000., 'ecog').set_montage(montage)
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_alignment`, which visualizes electrode positions.
fig = plot_alignment(info,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['pial'],
meg=False)
set_3d_view(figure=fig, azimuth=200, elevation=70)
xy, im = snapshot_brain_montage(fig, montage)
# 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')
def test_plot_alignment_basic(tmpdir, renderer, mixed_fwd_cov_evoked):
"""Test plotting of -trans.fif files and MEG sensor layouts."""
# generate fiducials file for testing
tempdir = str(tmpdir)
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)
evoked = read_evokeds(evoked_fname)[0]
info = evoked.info
sample_src = read_source_spaces(src_fname)
pytest.raises(TypeError,
plot_alignment,
'foo',
trans_fname,
subject='sample',
subjects_dir=subjects_dir)
pytest.raises(OSError,
plot_alignment,
info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
src='foo')
pytest.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')
# mixed source space
mixed_src = mixed_fwd_cov_evoked[0]['src']
assert mixed_src.kind == 'mixed'
plot_alignment(info,
meg=['helmet', 'sensors'],
dig=True,
coord_frame='head',
trans=Path(trans_fname),
subject='sample',
mri_fiducials=fiducials_path,
subjects_dir=subjects_dir,
src=mixed_src)
renderer.backend._close_all()
# no-head version
renderer.backend._close_all()
# trans required
with pytest.raises(ValueError, match='transformation matrix is required'):
plot_alignment(info, trans=None, src=src_fname)
with pytest.raises(ValueError, match='transformation matrix is required'):
plot_alignment(info, trans=None, mri_fiducials=True)
with pytest.raises(ValueError, match='transformation matrix is required'):
plot_alignment(info, trans=None, surfaces=['brain'])
# all coord frames
plot_alignment(info) # works: surfaces='auto' default
for coord_frame in ('meg', 'head', 'mri'):
fig = plot_alignment(info,
meg=['helmet', 'sensors'],
dig=True,
coord_frame=coord_frame,
trans=Path(trans_fname),
subject='sample',
mri_fiducials=fiducials_path,
subjects_dir=subjects_dir,
src=src_fname)
renderer.backend._close_all()
# EEG only with strange options
evoked_eeg_ecog_seeg = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog_seeg.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog_seeg.set_channel_types({
'EEG 001': 'ecog',
'EEG 002': 'seeg'
})
with catch_logging() as log:
plot_alignment(evoked_eeg_ecog_seeg.info,
subject='sample',
trans=trans_fname,
subjects_dir=subjects_dir,
surfaces=['white', 'outer_skin', 'outer_skull'],
meg=['helmet', 'sensors'],
eeg=['original', 'projected'],
ecog=True,
seeg=True,
verbose=True)
log = log.getvalue()
assert 'ecog: 1' in log
assert 'seeg: 1' in log
renderer.backend._close_all()
sphere = make_sphere_model(info=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',
eeg='projected',
meg='helmet',
bem=sphere,
dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull', 'outer_skin'])
plot_alignment(info,
subject='sample',
meg='helmet',
subjects_dir=subjects_dir,
eeg='projected',
bem=sphere,
surfaces=['head', 'brain'],
src=sample_src)
# no trans okay, no mri surfaces
plot_alignment(info, bem=sphere, surfaces=['brain'])
with pytest.raises(ValueError, match='A head surface is required'):
plot_alignment(info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
eeg='projected',
surfaces=[])
with pytest.raises(RuntimeError, match='No brain surface found'):
plot_alignment(info,
trans=trans_fname,
subject='foo',
subjects_dir=subjects_dir,
surfaces=['brain'])
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
plot_alignment(info,
trans_fname,
subject='sample',
meg=[],
subjects_dir=subjects_dir,
bem=bem_sol,
eeg=True,
surfaces=['head', 'inflated', 'outer_skull', 'inner_skull'])
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
plot_alignment(info,
trans_fname,
subject='sample',
meg=True,
subjects_dir=subjects_dir,
surfaces=['head', 'inner_skull'],
bem=bem_surfs)
# single-layer BEM can still plot head surface
assert bem_surfs[-1]['id'] == FIFF.FIFFV_BEM_SURF_ID_BRAIN
bem_sol_homog = read_bem_solution(
op.join(subjects_dir, 'sample', 'bem', 'sample-1280-bem-sol.fif'))
for use_bem in (bem_surfs[-1:], bem_sol_homog):
with catch_logging() as log:
plot_alignment(info,
trans_fname,
subject='sample',
meg=True,
subjects_dir=subjects_dir,
surfaces=['head', 'inner_skull'],
bem=use_bem,
verbose=True)
log = log.getvalue()
assert 'not find the surface for head in the provided BEM model' in log
# sphere model
sphere = make_sphere_model('auto', 'auto', info)
src = setup_volume_source_space(sphere=sphere)
plot_alignment(
info,
trans=Transform('head', 'mri'),
eeg='projected',
meg='helmet',
bem=sphere,
src=src,
dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull', 'outer_skin'])
sphere = make_sphere_model('auto', None, info) # one layer
# if you ask for a brain surface with a 1-layer sphere model it's an error
with pytest.raises(RuntimeError, match='Sphere model does not have'):
fig = plot_alignment(trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['brain'],
bem=sphere)
# but you can ask for a specific brain surface, and
# no info is permitted
fig = plot_alignment(trans=trans_fname,
subject='sample',
meg=False,
coord_frame='mri',
subjects_dir=subjects_dir,
surfaces=['white'],
bem=sphere,
show_axes=True)
renderer.backend._close_all()
if renderer._get_3d_backend() == 'mayavi':
import mayavi # noqa: F401 analysis:ignore
assert isinstance(fig, mayavi.core.scene.Scene)
# 3D coil with no defined draw (ConvexHull)
info_cube = pick_info(info, np.arange(6))
info['dig'] = None
info_cube['chs'][0]['coil_type'] = 9999
info_cube['chs'][1]['coil_type'] = 9998
with pytest.raises(RuntimeError, match='coil definition not found'):
plot_alignment(info_cube, meg='sensors', surfaces=())
coil_def_fname = op.join(tempdir, 'temp')
with open(coil_def_fname, 'w') as fid:
fid.write(coil_3d)
# make sure our other OPMs can be plotted, too
for ii, kind in enumerate(
('QUSPIN_ZFOPM_MAG', 'QUSPIN_ZFOPM_MAG2', 'FIELDLINE_OPM_MAG_GEN1',
'KERNEL_OPM_MAG_GEN1'), 2):
info_cube['chs'][ii]['coil_type'] = getattr(FIFF, f'FIFFV_COIL_{kind}')
with use_coil_def(coil_def_fname):
with catch_logging() as log:
plot_alignment(info_cube,
meg='sensors',
surfaces=(),
dig=True,
verbose='debug')
log = log.getvalue()
assert 'planar geometry' in log
# one layer bem with skull surfaces:
with pytest.raises(RuntimeError, match='Sphere model does not.*boundary'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['brain', 'head', 'inner_skull'],
bem=sphere)
# wrong eeg value:
with pytest.raises(ValueError, match='Invalid value for the .eeg'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
eeg='foo')
# wrong meg value:
with pytest.raises(ValueError, match='Invalid value for the .meg'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
meg='bar')
# multiple brain surfaces:
with pytest.raises(ValueError, match='Only one brain surface can be plot'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['white', 'pial'])
with pytest.raises(TypeError, match='surfaces.*must be'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=[1])
with pytest.raises(ValueError, match='Unknown surface type'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['foo'])
with pytest.raises(TypeError, match="must be an instance of "):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=dict(brain='super clear'))
with pytest.raises(ValueError, match="must be between 0 and 1"):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=dict(brain=42))
fwd_fname = op.join(data_dir, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif')
fwd = read_forward_solution(fwd_fname)
plot_alignment(subject='sample',
subjects_dir=subjects_dir,
trans=trans_fname,
fwd=fwd,
surfaces='white',
coord_frame='head')
fwd = convert_forward_solution(fwd, force_fixed=True)
plot_alignment(subject='sample',
subjects_dir=subjects_dir,
trans=trans_fname,
fwd=fwd,
surfaces='white',
coord_frame='head')
fwd['coord_frame'] = FIFF.FIFFV_COORD_MRI # check required to get to MRI
with pytest.raises(ValueError, match='transformation matrix is required'):
plot_alignment(info, trans=None, fwd=fwd)
# surfaces as dict
plot_alignment(subject='sample',
coord_frame='head',
trans=trans_fname,
subjects_dir=subjects_dir,
surfaces={
'white': 0.4,
'outer_skull': 0.6,
'head': None
})
'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_2017-04-14-10h-38m-59s_Phantom_1k_HPI_1s.bin')),
}
for system, raw in sorted(raws.items()):
meg = ['helmet', 'sensors']
# We don't have coil definitions for KIT refs, so exclude them
if system != 'KIT':
meg.append('ref')
fig = plot_alignment(raw.info, trans=None, dig=False, eeg=False,
surfaces=[], meg=meg, coord_frame='meg',
verbose=True)
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
text.property.font_size = 40
mlab.draw(fig)
