def test_3d_functions(renderer):
"""Test figure management functions."""
fig = renderer.create_3d_figure((300, 300))
# Mayavi actually needs something in the display to set the title
wrap_renderer = renderer._Renderer(fig=fig)
wrap_renderer.sphere(np.array([0., 0., 0.]), 'w', 1.)
renderer._check_3d_figure(fig)
renderer._set_3d_view(figure=fig, azimuth=None, elevation=None,
focalpoint=(0., 0., 0.), distance=None)
renderer._set_3d_title(figure=fig, title='foo')
renderer._take_3d_screenshot(figure=fig)
renderer._close_all()
def test_3d_backend(renderer):
"""Test default plot."""
# set data
win_size = (600, 600)
win_color = 'black'
tet_size = 1.0
tet_x = np.array([0, tet_size, 0, 0])
tet_y = np.array([0, 0, tet_size, 0])
tet_z = np.array([0, 0, 0, tet_size])
tet_indices = np.array([[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]])
tet_color = 'white'
sph_center = np.column_stack((tet_x, tet_y, tet_z))
sph_color = 'red'
sph_scale = tet_size / 3.0
ct_scalars = np.array([0.0, 0.0, 0.0, 1.0])
ct_levels = [0.2, 0.4, 0.6, 0.8]
ct_surface = {"rr": sph_center, "tris": tet_indices}
qv_mode = "arrow"
qv_color = 'blue'
qv_scale = tet_size / 2.0
qv_center = np.array([
np.mean((sph_center[va, :], sph_center[vb, :], sph_center[vc, :]),
axis=0) for (va, vb, vc) in tet_indices
])
center = np.mean(qv_center, axis=0)
qv_dir = qv_center - center
qv_scale_mode = 'scalar'
qv_scalars = np.linspace(1.0, 2.0, 4)
txt_x = 0.0
txt_y = 0.0
txt_text = "renderer"
txt_size = 14
cam_distance = 5 * tet_size
# init scene
rend = renderer._Renderer(size=win_size, bgcolor=win_color)
rend.set_interactive()
# use mesh
rend.mesh(x=tet_x,
y=tet_y,
z=tet_z,
triangles=tet_indices,
color=tet_color)
# use contour
rend.contour(surface=ct_surface,
scalars=ct_scalars,
contours=ct_levels,
kind='line')
rend.contour(surface=ct_surface,
scalars=ct_scalars,
contours=ct_levels,
kind='tube')
# use sphere
rend.sphere(center=sph_center,
color=sph_color,
scale=sph_scale,
radius=1.0)
# use quiver3d
rend.quiver3d(x=qv_center[:, 0],
y=qv_center[:, 1],
z=qv_center[:, 2],
u=qv_dir[:, 0],
v=qv_dir[:, 1],
w=qv_dir[:, 2],
color=qv_color,
scale=qv_scale,
scale_mode=qv_scale_mode,
scalars=qv_scalars,
mode=qv_mode)
# use tube
rend.tube(origin=np.array([[0, 0, 0]]), destination=np.array([[0, 1, 0]]))
tube = rend.tube(origin=np.array([[1, 0, 0]]),
destination=np.array([[1, 1, 0]]),
scalars=np.array([[1.0, 1.0]]))
# scalar bar
rend.scalarbar(source=tube, title="Scalar Bar")
# use text
rend.text2d(x_window=txt_x,
y_window=txt_y,
text=txt_text,
size=txt_size,
justification='right')
rend.text3d(x=0, y=0, z=0, text=txt_text, scale=1.0)
rend.set_camera(azimuth=180.0,
elevation=90.0,
distance=cam_distance,
focalpoint=center)
rend.show()
def test_plot_alignment(tmpdir, backends_3d):
"""Test plotting of -trans.fif files and MEG sensor layouts."""
from mne.viz.backends.renderer import _Renderer
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')
fig = plot_alignment(info, trans_fname, subject='sample',
subjects_dir=subjects_dir, meg=meg)
renderer = _Renderer(fig=fig)
renderer.close()
# 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'):
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 = _Renderer(fig=fig)
renderer.close()
# 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)
def test_3d_backend(backends_3d):
"""Test default plot."""
from mne.viz.backends.renderer import _Renderer
# set data
win_size = (600, 600)
win_color = (0, 0, 0)
tet_size = 1.0
tet_x = np.array([0, tet_size, 0, 0])
tet_y = np.array([0, 0, tet_size, 0])
tet_z = np.array([0, 0, 0, tet_size])
tet_indices = np.array([[0, 1, 2],
[0, 1, 3],
[0, 2, 3],
[1, 2, 3]])
tet_color = (1, 1, 1)
sph_center = np.column_stack((tet_x, tet_y, tet_z))
sph_color = (1, 0, 0)
sph_scale = tet_size / 3.0
ct_scalars = np.array([0.0, 0.0, 0.0, 1.0])
ct_levels = [0.2, 0.4, 0.6, 0.8]
ct_surface = {
"rr": sph_center,
"tris": tet_indices
}
qv_mode = "arrow"
qv_color = (0, 0, 1)
qv_scale = tet_size / 2.0
qv_center = np.array([np.mean((sph_center[va, :],
sph_center[vb, :],
sph_center[vc, :]), axis=0)
for (va, vb, vc) in tet_indices])
center = np.mean(qv_center, axis=0)
qv_dir = qv_center - center
qv_scale_mode = 'scalar'
qv_scalars = np.linspace(1.0, 2.0, 4)
txt_x = 0.0
txt_y = 0.0
txt_text = "renderer"
txt_width = 1.0
cam_distance = 5 * tet_size
# init scene
renderer = _Renderer(size=win_size, bgcolor=win_color)
renderer.set_interactive()
# use mesh
renderer.mesh(x=tet_x, y=tet_y, z=tet_z,
triangles=tet_indices,
color=tet_color)
# use contour
renderer.contour(surface=ct_surface, scalars=ct_scalars,
contours=ct_levels)
# use sphere
renderer.sphere(center=sph_center, color=sph_color,
scale=sph_scale)
# use quiver3d
renderer.quiver3d(x=qv_center[:, 0],
y=qv_center[:, 1],
z=qv_center[:, 2],
u=qv_dir[:, 0],
v=qv_dir[:, 1],
w=qv_dir[:, 2],
color=qv_color,
scale=qv_scale,
scale_mode=qv_scale_mode,
scalars=qv_scalars,
mode=qv_mode)
# use text
renderer.text(x=txt_x, y=txt_y, text=txt_text, width=txt_width)
renderer.set_camera(azimuth=180.0, elevation=90.0,
distance=cam_distance,
focalpoint=center)
renderer.show()
def test_plot_alignment(tmpdir, backends_3d):
"""Test plotting of -trans.fif files and MEG sensor layouts."""
from mne.viz.backends.renderer import _Renderer
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')
fig = plot_alignment(info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
meg=meg)
renderer = _Renderer(fig=fig)
renderer.close()
# 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'):
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 = _Renderer(fig=fig)
renderer.close()
# 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)
def test_3d_backend(renderer):
"""Test default plot."""
# set data
win_size = (600, 600)
win_color = (0, 0, 0)
tet_size = 1.0
tet_x = np.array([0, tet_size, 0, 0])
tet_y = np.array([0, 0, tet_size, 0])
tet_z = np.array([0, 0, 0, tet_size])
tet_indices = np.array([[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]])
tet_color = (1, 1, 1)
sph_center = np.column_stack((tet_x, tet_y, tet_z))
sph_color = (1, 0, 0)
sph_scale = tet_size / 3.0
ct_scalars = np.array([0.0, 0.0, 0.0, 1.0])
ct_levels = [0.2, 0.4, 0.6, 0.8]
ct_surface = {"rr": sph_center, "tris": tet_indices}
qv_mode = "arrow"
qv_color = (0, 0, 1)
qv_scale = tet_size / 2.0
qv_center = np.array([
np.mean((sph_center[va, :], sph_center[vb, :], sph_center[vc, :]),
axis=0) for (va, vb, vc) in tet_indices
])
center = np.mean(qv_center, axis=0)
qv_dir = qv_center - center
qv_scale_mode = 'scalar'
qv_scalars = np.linspace(1.0, 2.0, 4)
txt_x = 0.0
txt_y = 0.0
txt_text = "renderer"
txt_width = 1.0
cam_distance = 5 * tet_size
# init scene
rend = renderer._Renderer(size=win_size, bgcolor=win_color)
rend.set_interactive()
# use mesh
rend.mesh(x=tet_x,
y=tet_y,
z=tet_z,
triangles=tet_indices,
color=tet_color)
# use contour
rend.contour(surface=ct_surface, scalars=ct_scalars, contours=ct_levels)
# use sphere
rend.sphere(center=sph_center, color=sph_color, scale=sph_scale)
# use quiver3d
rend.quiver3d(x=qv_center[:, 0],
y=qv_center[:, 1],
z=qv_center[:, 2],
u=qv_dir[:, 0],
v=qv_dir[:, 1],
w=qv_dir[:, 2],
color=qv_color,
scale=qv_scale,
scale_mode=qv_scale_mode,
scalars=qv_scalars,
mode=qv_mode)
# use tube
rend.tube(origin=np.array([[0, 0, 0]]), destination=np.array([[0, 1, 0]]))
rend.tube(origin=np.array([[1, 0, 0]]),
destination=np.array([[1, 1, 0]]),
scalars=np.array([[1.0, 1.0]]))
# use text
rend.text2d(x=txt_x, y=txt_y, text=txt_text, width=txt_width)
if renderer.get_3d_backend() == "pyvista":
with pytest.raises(NotImplementedError):
rend.text3d(x=0, y=0, z=0, text=txt_text, scale=1.0)
else:
rend.text3d(x=0, y=0, z=0, text=txt_text, scale=1.0)
rend.set_camera(azimuth=180.0,
elevation=90.0,
distance=cam_distance,
focalpoint=center)
rend.show()
def test_3d_backend():
"""Test 3d backend degenerate scenarios and default plot."""
pytest.raises(ValueError, set_3d_backend, "unknown_backend")
pytest.raises(TypeError, set_3d_backend, 1)
assert get_3d_backend() == "mayavi"
# smoke test
set_3d_backend('mayavi')
set_3d_backend('mayavi')
# set data
win_size = (600, 600)
win_color = (0, 0, 0)
tet_size = 1.0
tet_x = np.array([0, tet_size, 0, 0])
tet_y = np.array([0, 0, tet_size, 0])
tet_z = np.array([0, 0, 0, tet_size])
tet_indices = np.array([[0, 1, 2],
[0, 1, 3],
[0, 2, 3],
[1, 2, 3]])
tet_color = (1, 1, 1)
sph_center = np.column_stack((tet_x, tet_y, tet_z))
sph_color = (1, 0, 0)
sph_scale = tet_size / 3.0
qv_mode = "arrow"
qv_color = (0, 0, 1)
qv_scale = tet_size / 2.0
qv_center = np.array([np.mean((sph_center[va, :],
sph_center[vb, :],
sph_center[vc, :]), axis=0)
for (va, vb, vc) in tet_indices])
center = np.mean(qv_center, axis=0)
qv_dir = qv_center - center
txt_x = 0.0
txt_y = 0.0
txt_text = "renderer"
txt_width = 1.0
cam_distance = 5 * tet_size
# init scene
renderer = _Renderer(size=win_size, bgcolor=win_color)
renderer.set_interactive()
# use mesh
renderer.mesh(x=tet_x, y=tet_y, z=tet_z,
triangles=tet_indices,
color=tet_color)
# use sphere
renderer.sphere(center=sph_center, color=sph_color,
scale=sph_scale)
# use quiver3d
renderer.quiver3d(x=qv_center[:, 0],
y=qv_center[:, 1],
z=qv_center[:, 2],
u=qv_dir[:, 0],
v=qv_dir[:, 1],
w=qv_dir[:, 2],
color=qv_color,
scale=qv_scale,
mode=qv_mode)
# use text
renderer.text(x=txt_x, y=txt_y, text=txt_text, width=txt_width)
renderer.set_camera(azimuth=180.0, elevation=90.0, distance=cam_distance,
focalpoint=center)
renderer.show()
def test_3d_backend(backends_3d):
"""Test default plot."""
from mne.viz.backends.renderer import _Renderer
# set data
win_size = (600, 600)
win_color = (0, 0, 0)
tet_size = 1.0
tet_x = np.array([0, tet_size, 0, 0])
tet_y = np.array([0, 0, tet_size, 0])
tet_z = np.array([0, 0, 0, tet_size])
tet_indices = np.array([[0, 1, 2],
[0, 1, 3],
[0, 2, 3],
[1, 2, 3]])
tet_color = (1, 1, 1)
sph_center = np.column_stack((tet_x, tet_y, tet_z))
sph_color = (1, 0, 0)
sph_scale = tet_size / 3.0
ct_scalars = np.array([0.0, 0.0, 0.0, 1.0])
ct_levels = [0.2, 0.4, 0.6, 0.8]
ct_surface = {
"rr": sph_center,
"tris": tet_indices
}
qv_mode = "arrow"
qv_color = (0, 0, 1)
qv_scale = tet_size / 2.0
qv_center = np.array([np.mean((sph_center[va, :],
sph_center[vb, :],
sph_center[vc, :]), axis=0)
for (va, vb, vc) in tet_indices])
center = np.mean(qv_center, axis=0)
qv_dir = qv_center - center
qv_scale_mode = 'scalar'
qv_scalars = np.linspace(1.0, 2.0, 4)
txt_x = 0.0
txt_y = 0.0
txt_text = "renderer"
txt_width = 1.0
cam_distance = 5 * tet_size
# init scene
renderer = _Renderer(size=win_size, bgcolor=win_color)
renderer.set_interactive()
# use mesh
renderer.mesh(x=tet_x, y=tet_y, z=tet_z,
triangles=tet_indices,
color=tet_color)
# use contour
renderer.contour(surface=ct_surface, scalars=ct_scalars,
contours=ct_levels)
# use sphere
renderer.sphere(center=sph_center, color=sph_color,
scale=sph_scale)
# use quiver3d
renderer.quiver3d(x=qv_center[:, 0],
y=qv_center[:, 1],
z=qv_center[:, 2],
u=qv_dir[:, 0],
v=qv_dir[:, 1],
w=qv_dir[:, 2],
color=qv_color,
scale=qv_scale,
scale_mode=qv_scale_mode,
scalars=qv_scalars,
mode=qv_mode)
# use text
renderer.text(x=txt_x, y=txt_y, text=txt_text, width=txt_width)
renderer.set_camera(azimuth=180.0, elevation=90.0,
distance=cam_distance,
focalpoint=center)
renderer.show()