1""" 2Plot Haxby masks 3================= 4 5Small script to plot the masks of the Haxby dataset. 6""" 7import matplotlib.pyplot as plt 8 9from nilearn import datasets 10haxby_dataset = datasets.fetch_haxby() 11 12# print basic information on the dataset 13print('First subject anatomical nifti image (3D) is at: %s' % 14 haxby_dataset.anat[0]) 15print('First subject functional nifti image (4D) is at: %s' % 16 haxby_dataset.func[0]) # 4D data 17 18# Build the mean image because we have no anatomic data 19from nilearn import image 20func_filename = haxby_dataset.func[0] 21mean_img = image.mean_img(func_filename) 22 23z_slice = -14 24 25fig = plt.figure(figsize=(4, 5.4), facecolor='k') 26 27from nilearn.plotting import plot_anat, show 28display = plot_anat(mean_img, display_mode='z', cut_coords=[z_slice], 29 figure=fig) 30mask_vt_filename = haxby_dataset.mask_vt[0] 31mask_house_filename = haxby_dataset.mask_house[0] 32mask_face_filename = haxby_dataset.mask_face[0] 33display.add_contours(mask_vt_filename, contours=1, antialiased=False, 34 linewidths=4., levels=[0], colors=['red']) 35display.add_contours(mask_house_filename, contours=1, antialiased=False, 36 linewidths=4., levels=[0], colors=['blue']) 37display.add_contours(mask_face_filename, contours=1, antialiased=False, 38 linewidths=4., levels=[0], colors=['limegreen']) 39 40# We generate a legend using the trick described on 41# http://matplotlib.sourceforge.net/users/legend_guide.httpml#using-proxy-artist 42from matplotlib.patches import Rectangle 43p_v = Rectangle((0, 0), 1, 1, fc="red") 44p_h = Rectangle((0, 0), 1, 1, fc="blue") 45p_f = Rectangle((0, 0), 1, 1, fc="limegreen") 46plt.legend([p_v, p_h, p_f], ["vt", "house", "face"]) 47 48show() 49