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%matplotlib inline

Basic nilearn example: manipulating and looking at data

A simple example showing how to load an existing Nifti file and use basic nilearn functionalities.



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# Let us use a Nifti file that is shipped with nilearn
from nilearn.datasets import MNI152_FILE_PATH

# Note that the variable MNI152_FILE_PATH is just a path to a Nifti file
print('Path to MNI152 template: %r' % MNI152_FILE_PATH)

A first step: looking at our data

Let's quickly plot this file:



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from nilearn import plotting
plotting.plot_img(MNI152_FILE_PATH)

This is not a very pretty plot. We just used the simplest possible code. There is a whole section of the documentation <plotting> on making prettier code.

Exercise: Try plotting one of your own files. In the above, MNI152_FILE_PATH is nothing more than a string with a path pointing to a nifti image. You can replace it with a string pointing to a file on your disk. Note that it should be a 3D volume, and not a 4D volume.

Simple image manipulation: smoothing

Let's use an image-smoothing function from nilearn: :func:nilearn.image.smooth_img

Functions containing 'img' can take either a filename or an image as input.

Here we give as inputs the image filename and the smoothing value in mm



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from nilearn import image
smooth_anat_img = image.smooth_img(MNI152_FILE_PATH, fwhm=3)

# While we are giving a file name as input, the function returns
# an in-memory object:
print(smooth_anat_img)

This is an in-memory object. We can pass it to nilearn function, for instance to look at it



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plotting.plot_img(smooth_anat_img)

We could also pass it to the smoothing function



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more_smooth_anat_img = image.smooth_img(smooth_anat_img, fwhm=3)
plotting.plot_img(more_smooth_anat_img)

Saving results to a file

We can save any in-memory object as follows:



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more_smooth_anat_img.to_filename('more_smooth_anat_img.nii.gz')

Finally, calling plotting.show() is necessary to display the figure when running as a script outside IPython



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plotting.show()

To recap, all the nilearn tools can take data as filenames or in-memory objects, and return brain volumes as in-memory objects. These can be passed on to other nilearn tools, or saved to disk.