.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/01_plotting/plot_haxby_masks.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_01_plotting_plot_haxby_masks.py: Plot Haxby masks ================ Small script to plot the masks of the Haxby dataset. .. GENERATED FROM PYTHON SOURCE LINES 9-11 Load Haxby dataset ------------------ .. GENERATED FROM PYTHON SOURCE LINES 11-32 .. code-block:: Python 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) z_slice = -14 .. rst-class:: sphx-glr-script-out .. code-block:: none First subject anatomical nifti image (3D) is at: /home/remi/nilearn_data/haxby2001/subj2/anat.nii.gz First subject functional nifti image (4D) is at: /home/remi/nilearn_data/haxby2001/subj2/bold.nii.gz .. GENERATED FROM PYTHON SOURCE LINES 33-35 Plot the masks -------------- .. GENERATED FROM PYTHON SOURCE LINES 35-68 .. code-block:: Python 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() .. image-sg:: /auto_examples/01_plotting/images/sphx_glr_plot_haxby_masks_001.png :alt: plot haxby masks :srcset: /auto_examples/01_plotting/images/sphx_glr_plot_haxby_masks_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/remi/github/nilearn/nilearn/env/lib/python3.11/site-packages/nilearn/plotting/displays/_axes.py:74: UserWarning: The following kwargs were not used by contour: 'contours', 'linewidth' im = getattr(ax, type)( .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 9.327 seconds) **Estimated memory usage:** 916 MB .. _sphx_glr_download_auto_examples_01_plotting_plot_haxby_masks.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn/0.10.3?urlpath=lab/tree/notebooks/auto_examples/01_plotting/plot_haxby_masks.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_haxby_masks.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_haxby_masks.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_