# can be applied to Cohen’s D (C as done here) or
# statistical values (statscondCluster.F_obs or F_obs_plot)
# of inter-individual brain connectivity
# defining manually bad channel for viz test
epo1.info['bads'] = ['F8', 'Fp2', 'Cz', 'O2']
epo2.info['bads'] = ['F7', 'O1']
# Visualization of inter-brain connectivity in 2D
# defining head model and adding sensors
fig, ax = plt.subplots(1, 1)
ax.axis("off")
vertices, faces = viz.get_3d_heads()
camera = Camera("ortho", theta=90, phi=180, scale=1)
mesh = Mesh(ax, camera.transform @ glm.yrotate(90), vertices, faces,
facecolors='white', edgecolors='black', linewidths=.25)
camera.connect(ax, mesh.update)
plt.gca().set_aspect('equal', 'box')
plt.axis('off')
viz.plot_sensors_2d(epo1, epo2, lab=True) # bads are represented as squares
# plotting links according to sign (red for positive values,
# blue for negative) and value (line thickness increases
# with the strength of connectivity)
viz.plot_links_2d(epo1, epo2, C=C, threshold=2, steps=10)
plt.tight_layout()
plt.show()
# Visualization of inter-brain connectivity in 3D
# defining head model and adding sensors
vertices, faces = viz.get_3d_heads()
mesh = Mesh(ax,
camera,
vertices,
faces,
facecolors=white,
edgecolors=black,
linewidths=.25)
ax.text(.99,
.99,
"Orthographic (XZ)",
transform=ax.transAxes,
ha="right",
va="top")
ax = subplot(223)
camera = ortho @ glm.yrotate(90)
mesh = Mesh(ax,
camera,
vertices,
faces,
facecolors=white,
edgecolors=black,
linewidths=.25)
ax.text(.99,
.99,
"Orthographic (XY)",
transform=ax.transAxes,
ha="right",
va="top")
ax = subplot(224)
import matplotlib.pyplot as plt
import nibabel as nb
fig = plt.figure(figsize=(6, 6))
ax = fig.add_axes([0, 0, 1, 1], xlim=[-1, +1], ylim=[-1, +1], aspect=1)
ax.axis("off")
vertices, faces = nb.freesurfer.io.read_geometry('data/lh.pial')
vertices = glm.fit_unit_cube(vertices)
facecolors = lighting(vertices[faces],
direction=(-1, 0, 0.25),
color=(1.0, 0.5, 0.5),
specular=True)
camera = glm.ortho(-1, +1, -1, +1, 1, 100)
camera = camera @ glm.scale(1.9) @ glm.yrotate(90) @ glm.xrotate(270)
start = time.time()
Mesh(ax,
camera,
vertices,
faces,
facecolors=facecolors,
linewidths=0,
mode="front")
elapsed = time.time() - start
text = "{0} vertices, {1} faces rendered in {2:.2f} second(s) with matplotlib"
text = text.format(len(vertices), len(faces), elapsed)
ax.text(0,
0,