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()