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February 28, 2019 17:40
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| def plot_surf_stat_multidim(coords, faces, face_colors=None, stat_map=None, | |
| elev=0, azim=0, | |
| cmap=None, | |
| threshold=None, bg_map=None, | |
| mask=None, | |
| bg_on_stat=False, | |
| alpha='auto', | |
| vmax=None, symmetric_cbar="auto", | |
| figsize=(14,11), label=None, lenient=None, | |
| **kwargs): | |
| ''' Visualize results on cortical surface using matplotlib''' | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import matplotlib.tri as tri | |
| from mpl_toolkits.mplot3d import Axes3D | |
| # load mesh and derive axes limits | |
| faces = np.array(faces, dtype=int) | |
| limits = [coords.min(), coords.max()] | |
| alpha = 1 | |
| fig = plt.figure(figsize=figsize) | |
| fig.patch.set_facecolor('white') | |
| ax1 = fig.add_subplot(111, projection='3d', xlim=limits, ylim=limits) | |
| ax1.grid(False) | |
| ax1.set_axis_off() | |
| ax1.w_zaxis.line.set_lw(0.) | |
| ax1.set_zticks([]) | |
| ax1.view_init(elev=elev, azim=azim) | |
| #ax1.set_axis_off() | |
| # plot mesh without data | |
| p3dcollec = ax1.plot_trisurf(coords[:, 0], coords[:, 1], coords[:, 2], | |
| triangles=faces, linewidth=0., | |
| antialiased=False, | |
| color='white') | |
| #face_colors = np.mean(multidim(faces), axis=0) | |
| p3dcollec.set_facecolors(face_colors) | |
| return fig | |
| # Load data: | |
| df = pd.DataFrame() | |
| df['e0'] = np.array(emb[range(cortLen),0]) | |
| df['e1'] = np.array(emb[range(cortLen),1]) * -1 # to reorient plot | |
| # Create rgba colors for each vertex: | |
| import matplotlib.cm as cm | |
| norm_1 = mpl.colors.Normalize(vmin=df['e0'].min(), vmax=df['e0'].max()) | |
| cmap_1 = cm.Reds | |
| map_1 = cm.ScalarMappable(norm=norm_1, cmap=cmap_1) | |
| norm_2 = mpl.colors.Normalize(vmin=df['e1'].min(), vmax=df['e1'].max()) | |
| cmap_2 = cm.Greens | |
| map_2 = cm.ScalarMappable(norm=norm_2, cmap=cmap_2) | |
| norm_3 = mpl.colors.Normalize(vmin=df['e1'].max() * -1, vmax=df['e1'].min() * -1) | |
| cmap_3 = cm.Blues | |
| map_3= cm.ScalarMappable(norm=norm_3, cmap=cmap_3) | |
| c = np.ones(map_1.to_rgba(df['e0']).shape) | |
| a = df['e1'].copy() | |
| a = a - a.min() | |
| a = a / a.max() | |
| c[:,1] = a | |
| a = df['e1'].copy() * -1 | |
| a = a - a.min() | |
| a = a / a.max() | |
| c[:,2] = a | |
| a = df['e0'].copy() | |
| a = a - a.min() | |
| a = a / a.max() | |
| c[:,0] = a | |
| c[:,1] = c[:,1] * ((a - 1) * -1) | |
| c[:,2] = c[:,2] * ((a - 1) * -1) | |
| # Project to faces and visualize: | |
| c = np.array(c) | |
| d = np.ones((32492*2,4)) | |
| d[cortL,:] = c[range(len(cortL)),:] | |
| bg_map=sulcL | |
| bg_faces = np.mean(bg_map[surfL[1]], axis=1) | |
| bg_faces = bg_faces - bg_faces.min() | |
| bg_faces = bg_faces / bg_faces.max() / 2. | |
| face_colors = plt.cm.gray_r(bg_faces) | |
| face_colors = np.mean(d[surfL[1]], axis=1) * face_colors | |
| f = plot_surf_stat_multidim(surfL[0], surfL[1], bg_map=sulcL, face_colors=face_colors, azim=0, alpha=1) | |
| f.savefig('gradient_data/figures/hcp.emb.0.multidim.lat.png') | |
| f = plot_surf_stat_multidim(surfL[0], surfL[1], bg_map=sulcL, face_colors=face_colors, azim=180, alpha=1) | |
| f.savefig('gradient_data/figures/hcp.emb.0.multidim.med.png') | |
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