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.

7.10.10. nilearn.plotting.plot_stat_map

nilearn.plotting.plot_stat_map(stat_map_img, bg_img=<MNI152Template>, cut_coords=None, output_file=None, display_mode='ortho', colorbar=True, figure=None, axes=None, title=None, threshold=1e-06, annotate=True, draw_cross=True, black_bg='auto', cmap=<matplotlib.colors.LinearSegmentedColormap object at 0x7f676c650c90>, symmetric_cbar='auto', dim='auto', vmax=None, resampling_interpolation='continuous', **kwargs)

Plot cuts of an ROI/mask image (by default 3 cuts: Frontal, Axial, and Lateral)

Parameters
stat_map_imgNiimg-like object

See http://nilearn.github.io/manipulating_images/input_output.html The statistical map image

bg_imgNiimg-like object

See http://nilearn.github.io/manipulating_images/input_output.html The background image that the ROI/mask will be plotted on top of. If nothing is specified, the MNI152 template will be used. To turn off background image, just pass “bg_img=None”.

cut_coordsNone, a tuple of floats, or an integer

The MNI coordinates of the point where the cut is performed If display_mode is ‘ortho’ or ‘tiled’, this should be a 3-tuple: (x, y, z) For display_mode == ‘x’, ‘y’, or ‘z’, then these are the coordinates of each cut in the corresponding direction. If None is given, the cuts is calculated automaticaly. If display_mode is ‘x’, ‘y’ or ‘z’, cut_coords can be an integer, in which case it specifies the number of cuts to perform

output_filestring, or None, optional

The name of an image file to export the plot to. Valid extensions are .png, .pdf, .svg. If output_file is not None, the plot is saved to a file, and the display is closed.

display_mode{‘ortho’, ‘tiled’, ‘x’, ‘y’, ‘z’, ‘yx’, ‘xz’, ‘yz’}

Choose the direction of the cuts: ‘x’ - sagittal, ‘y’ - coronal, ‘z’ - axial, ‘ortho’ - three cuts are performed in orthogonal directions, ‘tiled’ - three cuts are performed and arranged in a 2x2 grid.

colorbarboolean, optional

If True, display a colorbar on the right of the plots.

figureinteger or matplotlib figure, optional

Matplotlib figure used or its number. If None is given, a new figure is created.

axesmatplotlib axes or 4 tuple of float: (xmin, ymin, width, height), optional

The axes, or the coordinates, in matplotlib figure space, of the axes used to display the plot. If None, the complete figure is used.

titlestring, optional

The title displayed on the figure.

thresholda number, None, or ‘auto’

If None is given, the image is not thresholded. If a number is given, it is used to threshold the image: values below the threshold (in absolute value) are plotted as transparent. If auto is given, the threshold is determined magically by analysis of the image.

annotateboolean, optional

If annotate is True, positions and left/right annotation are added to the plot.

draw_crossboolean, optional

If draw_cross is True, a cross is drawn on the plot to indicate the cut plosition.

black_bgboolean, optional

If True, the background of the image is set to be black. If you wish to save figures with a black background, you will need to pass “facecolor=’k’, edgecolor=’k’” to matplotlib.pyplot.savefig.

cmapmatplotlib colormap, optional

The colormap for specified image. The ccolormap must be symmetrical.

symmetric_cbarboolean or ‘auto’, optional, default ‘auto’

Specifies whether the colorbar should range from -vmax to vmax or from vmin to vmax. Setting to ‘auto’ will select the latter if the range of the whole image is either positive or negative. Note: The colormap will always be set to range from -vmax to vmax.

dimfloat, ‘auto’ (by default), optional

Dimming factor applied to background image. By default, automatic heuristics are applied based upon the background image intensity. Accepted float values, where a typical scan is between -2 and 2 (-2 = increase constrast; 2 = decrease contrast), but larger values can be used for a more pronounced effect. 0 means no dimming.

vmaxfloat

Upper bound for plotting, passed to matplotlib.pyplot.imshow

resampling_interpolationstr

Interpolation to use when resampling the image to the destination space. Can be “continuous” (default) to use 3rd-order spline interpolation, or “nearest” to use nearest-neighbor mapping. “nearest” is faster but can be noisier in some cases.

See also

nilearn.plotting.plot_anat

To simply plot anatomical images

nilearn.plotting.plot_epi

To simply plot raw EPI images

nilearn.plotting.plot_glass_brain

To plot maps in a glass brain

Notes

Arrays should be passed in numpy convention: (x, y, z) ordered.

For visualization, non-finite values found in passed ‘stat_map_img’ or ‘bg_img’ are set to zero.

7.10.10.1. Examples using nilearn.plotting.plot_stat_map