Note
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Basic Atlas plotting#
Plot the regions of a reference atlas (Harvard-Oxford and Juelich atlases).
Retrieving the atlas data#
from nilearn import datasets
dataset_ho = datasets.fetch_atlas_harvard_oxford('cort-maxprob-thr25-2mm')
dataset_ju = datasets.fetch_atlas_juelich('maxprob-thr0-1mm')
atlas_ho_filename = dataset_ho.filename
atlas_ju_filename = dataset_ju.filename
print('Atlas ROIs are located at: %s' % atlas_ho_filename)
print('Atlas ROIs are located at: %s' % atlas_ju_filename)
Atlas ROIs are located at: /home/yasmin/nilearn/nilearn_data/fsl/data/atlases/HarvardOxford/HarvardOxford-cort-maxprob-thr25-2mm.nii.gz
Atlas ROIs are located at: /home/yasmin/nilearn/nilearn_data/fsl/data/atlases/Juelich/Juelich-maxprob-thr0-1mm.nii.gz
Visualizing the Harvard-Oxford atlas#
from nilearn import plotting
plotting.plot_roi(atlas_ho_filename, title="Harvard Oxford atlas")
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fc1e6394070>
Visualizing the Juelich atlas#
plotting.plot_roi(atlas_ju_filename, title="Juelich atlas")
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fc1e39c96f0>
Visualizing the Harvard-Oxford atlas with contours#
plotting.plot_roi(atlas_ho_filename, view_type='contours',
title="Harvard Oxford atlas in contours")
plotting.show()
/home/yasmin/nilearn/nilearn/nilearn/plotting/img_plotting.py:572: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
img = new_img_like(roi_img, data, affine=roi_img.affine)
/home/yasmin/nilearn/nilearn/nilearn/plotting/displays/_axes.py:71: UserWarning: No contour levels were found within the data range.
im = getattr(ax, type)(data_2d.copy(),
Visualizing the Juelich atlas with contours#
plotting.plot_roi(atlas_ju_filename, view_type='contours',
title="Juelich atlas in contours")
plotting.show()
/home/yasmin/nilearn/nilearn/nilearn/plotting/img_plotting.py:572: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
img = new_img_like(roi_img, data, affine=roi_img.affine)
/home/yasmin/nilearn/nilearn/nilearn/plotting/displays/_axes.py:71: UserWarning: No contour levels were found within the data range.
im = getattr(ax, type)(data_2d.copy(),
Total running time of the script: ( 1 minutes 15.912 seconds)
Estimated memory usage: 234 MB
