Plot Haxby masks

Small script to plot the masks of the Haxby dataset.

Load Haxby dataset

from nilearn import datasets
from nilearn.plotting import plot_anat, show

haxby_dataset = datasets.fetch_haxby()

# print basic information on the dataset
print(
    f"First subject anatomical nifti image (3D) is at: {haxby_dataset.anat[0]}"
)
print(
    f"First subject functional nifti image (4D) is at: {haxby_dataset.func[0]}"
)

# Build the mean image because we have no anatomic data
from nilearn import image

func_filename = haxby_dataset.func[0]
mean_img = image.mean_img(func_filename, copy_header=True)

z_slice = -14

Plot the masks

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(4, 5.4), facecolor="k")

display = plot_anat(
    mean_img, display_mode="z", cut_coords=[z_slice], figure=fig
)
mask_vt_filename = haxby_dataset.mask_vt[0]
mask_house_filename = haxby_dataset.mask_house[0]
mask_face_filename = haxby_dataset.mask_face[0]
masks = [mask_vt_filename, mask_house_filename, mask_face_filename]
colors = ["red", "blue", "limegreen"]
for mask, color in zip(masks, colors):
    display.add_contours(
        mask,
        contours=1,
        antialiased=False,
        linewidth=4.0,
        levels=[0],
        colors=[color],
    )

# We generate a legend using the trick described on
# https://matplotlib.org/2.0.2/users/legend_guide.html
from matplotlib.patches import Rectangle

p_v = Rectangle((0, 0), 1, 1, fc="red")
p_h = Rectangle((0, 0), 1, 1, fc="blue")
p_f = Rectangle((0, 0), 1, 1, fc="limegreen")
plt.legend([p_v, p_h, p_f], ["vt", "house", "face"], loc="lower right")

show()

Estimated memory usage: 0 MB