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#

nilearn.plotting.plot_img(img, cut_coords=None, output_file=None, display_mode='ortho', figure=None, axes=None, title=None, threshold=None, annotate=True, draw_cross=True, black_bg=False, colorbar=False, cbar_tick_format='%.2g', resampling_interpolation='continuous', bg_img=None, vmin=None, vmax=None, **kwargs)[source]#

Plot cuts of a given image (by default Frontal, Axial, and Lateral)

Parameters

imgNiimg-like object

See input-output.

cut_coordsNone, a tuple of float, or int, optional

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 are calculated automatically.

If display_mode is ‘mosaic’, and the number of cuts is the same for all directions, cut_coords can be specified as an integer. It can also be a length 3 tuple specifying the number of cuts for every direction if these are different.

Note

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_filestr, 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’, ‘mosaic’,’x’,’y’, ‘z’, ‘yx’, ‘xz’, ‘yz’}, optional

Choose the direction of the cuts:

‘x’: sagital

‘y’: coronal

‘z’: axial

‘ortho’: three cuts are performed in orthogonal directions

‘tiled’: three cuts are performed and arranged in a 2x2 grid

‘mosaic’: three cuts are performed along multiple rows and columns

Default=’ortho’.

figureint, or matplotlib.figure.Figure, or None, optional

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

axesmatplotlib.axes.Axes, or 4 tupleof 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.

titlestr, or None, optional

The title displayed on the figure. Default=None.

thresholda number, None, or ‘auto’, optional

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.

annotatebool, optional

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

decimalsinteger, optional

Number of decimal places on slice position annotation. If False (default), the slice position is integer without decimal point.

draw_crossbool, optional

If draw_cross is True, a cross is drawn on the plot to indicate the cut position. Default=True.

black_bgbool, or ‘auto’, 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. Default=False.

colorbarbool, optional

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

cbar_tick_format: str, optional

Controls how to format the tick labels of the colorbar. Ex: use “%i” to display as integers. Default is ‘%.2g’ for scientific notation.

resampling_interpolationstr, optional

Interpolation to use when resampling the image to the destination space. Can be:

“continuous”: use 3rd-order spline interpolation

“nearest”: use nearest-neighbor mapping.

Note

“nearest” is faster but can be noisier in some cases.

Default=’continuous’.

bg_imgNiimg-like object, optional

See input_output. The background image to plot on top of. If nothing is specified, no background image is plotted. Default=None.

vminfloat, optional

Lower bound of the colormap. If None, the min of the image is used. Passed to matplotlib.pyplot.imshow.

vmaxfloat, optional

Upper bound of the colormap. If None, the max of the image is used. Passed to matplotlib.pyplot.imshow.

kwargsextra keyword arguments, optional

Extra keyword arguments passed to matplotlib.pyplot.imshow.

Examples using `nilearn.plotting.plot_img`#

Basic nilearn example: manipulating and looking at data

Intro to GLM Analysis: a single-session, single-subject fMRI dataset

Searchlight analysis of face vs house recognition

nilearn.plotting.plot_img#

Examples using nilearn.plotting.plot_img#

Examples using `nilearn.plotting.plot_img`#