well_x = 200 * np.ones(n_log_samples)
delta_x = 0.3 * np.random.randn(n_log_samples)
well_x += np.cumsum(delta_x)
# Get Y and Z coordinates.
well_y = 100 * np.ones(n_log_samples)
well_z = np.linspace(0, 80, n_log_samples)
# Concate X, Y, and Z coordinates.
well_log_coords = np.stack((well_x, well_y, well_z), axis=1)
# Get well log colors.
cmap = get_colormap('hsl')
values = np.random.uniform(-1.5, 2.5, n_log_samples)
values = np.convolve(values, np.ones((20,))/20, mode='same')
well_log_colors = np.array([cmap.map(x) for x in values]).squeeze()
well_log = Markers(pos=well_log_coords, symbol='hbar', size=15,
face_color=well_log_colors, edge_width=0)
well_log.set_gl_state(depth_test=False)
visual_nodes.append(well_log)
canvas4 = SeismicCanvas(title='Voting Scores',
visual_nodes=visual_nodes,
xyz_axis=xyz_axis,
colorbar=colorbar,
**canvas_params)
# Image 5: seismic with fault skin surfaces (meshes).
fault_surfaces = []
fault_cmap = 'hsl'
fault_range = (0, 180)
# Read from skin files using FaultSkin class.
