This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.


nilearn.plotting.plot_img_comparison(ref_imgs, src_imgs, masker, plot_hist=True, log=True, ref_label='image set 1', src_label='image set 2', output_dir=None, axes=None)[source]#

Create plots to compare two lists of images and measure correlation.

The first plot displays linear correlation between voxel values. The second plot superimposes histograms to compare values distribution.


Reference images.


Source images.

maskerNiftiMasker object

Mask to be used on data.

plot_histBoolean, default=True

If True then histograms of each img in ref_imgs will be plotted along-side the histogram of the corresponding image in src_imgs.

logBoolean, default=True

Passed to plt.hist.

ref_labelstr, default=’image set 1’

Name of reference images.

src_labelstr, default=’image set 2’

Name of source images.

output_dirstring, optional

Directory where plotted figures will be stored.

axeslist of two matplotlib Axes objects, optional

Can receive a list of the form [ax1, ax2] to render the plots. By default new axes will be created.


Pearson correlation between the images.

Examples using nilearn.plotting.plot_img_comparison#

First level analysis of a complete BIDS dataset from openneuro

First level analysis of a complete BIDS dataset from openneuro