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.plot_img_comparison#
- 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.
- Parameters:
- ref_imgsnifti_like
Reference images.
- src_imgsnifti_like
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.
- Returns:
- corrsnumpy.ndarray
Pearson correlation between the images.
Examples using nilearn.plotting.plot_img_comparison#
First level analysis of a complete BIDS dataset from openneuro