Note

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.img_comparison.plot_img_comparison

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

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.

Parameters:

ref_img3D Niimg-like object or SurfaceImage or a list of 3D Niimg-like object or SurfaceImage

Reference image.

src_img3D Niimg-like object or SurfaceImage or a list of 3D Niimg-like object or SurfaceImage

Source image. Its type must match that of the ref_img. If the source image is Niimg-Like, it will be resampled to match that or the source image.

masker3D Niimg-like binary mask or NiftiMasker or binary SurfaceImage or or SurfaceMasker or None

Mask to be used on data. Its type must be compatible with that of the ref_img. If None is passed, an appropriate masker will be fitted on the first reference image.

plot_histbool, 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.

logbool, 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_dirstr or None, default=None

Directory where plotted figures will be stored.

axeslist of two matplotlib Axes objects, or None, default=None

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

%(colorbar)s

default=True

Returns:

corrsnumpy.ndarray

Pearson correlation between the images.