.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/01_plotting/plot_prob_atlas.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_prob_atlas.py: Visualizing 4D probabilistic atlas maps ======================================= This example shows how to visualize probabilistic atlases made of 4D images. There are 3 different display types: 1. "contours", which means maps or ROIs are shown as contours delineated by colored lines. 2. "filled_contours", maps are shown as contours same as above but with fillings inside the contours. 3. "continuous", maps are shown as just color overlays. A colorbar can optionally be added. The :func:`nilearn.plotting.plot_prob_atlas` function displays each map with each different color which are picked randomly from the colormap which is already defined. See :ref:`plotting` for more information to know how to tune the parameters. .. GENERATED FROM PYTHON SOURCE LINES 26-27 Load 4D probabilistic atlases .. GENERATED FROM PYTHON SOURCE LINES 27-40 .. code-block:: Python from nilearn import datasets, plotting # Allen RSN networks allen = datasets.fetch_atlas_allen_2011() # ICBM tissue probability icbm = datasets.fetch_icbm152_2009() # Smith ICA BrainMap 2009 smith_bm20 = datasets.fetch_atlas_smith_2009(resting=False, dimension=20)[ "maps" ] .. GENERATED FROM PYTHON SOURCE LINES 41-42 Visualization .. GENERATED FROM PYTHON SOURCE LINES 42-60 .. code-block:: Python # "contours" example plotting.plot_prob_atlas(allen.rsn28, title="Allen2011") # "continuous" example plotting.plot_prob_atlas( (icbm["wm"], icbm["gm"], icbm["csf"]), title="ICBM tissues" ) # "filled_contours" example. An optional colorbar can be set. plotting.plot_prob_atlas( smith_bm20, title="Smith2009 20 Brainmap (with colorbar)", colorbar=True, ) plotting.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_001.png :alt: plot prob atlas :srcset: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_002.png :alt: plot prob atlas :srcset: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_003.png :alt: plot prob atlas :srcset: /auto_examples/01_plotting/images/sphx_glr_plot_prob_atlas_003.png :class: sphx-glr-multi-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: linewidths is ignored by contourf im = getattr(ax, type)( .. GENERATED FROM PYTHON SOURCE LINES 61-121 Other probabilistic atlases accessible with nilearn --------------------------------------------------- To save build time, the following code is not executed. Try running it locally to get the same plots as above for each of the listed atlases. .. code-block:: default # Harvard Oxford Atlas harvard_oxford = datasets.fetch_atlas_harvard_oxford("cort-prob-2mm") harvard_oxford_sub = datasets.fetch_atlas_harvard_oxford("sub-prob-2mm") # Smith ICA Atlas and Brain Maps 2009 smith_rsn10 = datasets.fetch_atlas_smith_2009( resting=True, dimension=10 )["maps"] smith_rsn20 = datasets.fetch_atlas_smith_2009( resting=True, dimension=20 )["maps"] smith_rsn70 = datasets.fetch_atlas_smith_2009( resting=True, dimension=70 )["maps"] smith_bm10 = datasets.fetch_atlas_smith_2009( resting=False, dimension=10 )["maps"] smith_bm70 = datasets.fetch_atlas_smith_2009( resting=False, dimension=70 )["maps"] # Multi Subject Dictionary Learning Atlas msdl = datasets.fetch_atlas_msdl() # Pauli subcortical atlas subcortex = datasets.fetch_atlas_pauli_2017() # Dictionaries of Functional Modes (“DiFuMo”) atlas dim = 64 res = 2 difumo = datasets.fetch_atlas_difumo( dimension=dim, resolution_mm=res, legacy_format=False ) # Visualization atlas_types = { "Harvard_Oxford": harvard_oxford.maps, "Harvard_Oxford sub": harvard_oxford_sub.maps, "Smith 2009 10 RSNs": smith_rsn10, "Smith2009 20 RSNs": smith_rsn20, "Smith2009 70 RSNs": smith_rsn70, "Smith2009 10 Brainmap": smith_bm10, "Smith2009 70 Brainmap": smith_bm70, "MSDL": msdl.maps, "Pauli2017 Subcortical Atlas": subcortex.maps, f"DiFuMo dimension {dim} resolution {res}": difumo.maps, } for name, atlas in sorted(atlas_types.items()): plotting.plot_prob_atlas(atlas, title=name) plotting.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 24.161 seconds) **Estimated memory usage:** 422 MB .. _sphx_glr_download_auto_examples_01_plotting_plot_prob_atlas.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_prob_atlas.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_prob_atlas.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_prob_atlas.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_