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, radiological=False, decimals=False, **kwargs)[source]¶
Plot cuts of a given image.
By default Frontal, Axial, and Lateral.
- Parameters:
- imgNiimg-like object
- cut_coordsNone, a
tuple
offloat
, orint
, 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_file
str
, 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”}, default=”ortho”
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
“mosaic”: three cuts are performed along multiple rows and columns
- figure
int
, ormatplotlib.figure.Figure
, or None, optional Matplotlib figure used or its number. If None is given, a new figure is created.
- axes
matplotlib.axes.Axes
, or 4 tupleoffloat
: (xmin, ymin, width, height), default=None The axes, or the coordinates, in matplotlib figure space, of the axes used to display the plot. If None, the complete figure is used.
- title
str
, or None, default=None The title displayed on the figure.
- 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.
- annotate
bool
, default=True If annotate is True, positions and left/right annotation are added to the plot.
- decimals
int
or bool, default=False Number of decimal places on slice position annotation. If False (default), the slice position is integer without decimal point.
- draw_cross
bool
, default=True If draw_cross is True, a cross is drawn on the plot to indicate the cut position.
- black_bg
bool
, 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.- colorbar
bool
, optional If True, display a colorbar on the right of the plots. Default=False.
- cbar_tick_format: str, default=”%.2g” (scientific notation)
Controls how to format the tick labels of the colorbar. Ex: use “%i” to display as integers.
- resampling_interpolation
str
, 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 and output: neuroimaging data representation. The background image to plot on top of. If nothing is specified, no background image is plotted. Default=None.
- vmin
float
, optional Lower bound of the colormap. If None, the min of the image is used. Passed to
matplotlib.pyplot.imshow
.- vmax
float
, optional Upper bound of the colormap. If None, the max of the image is used. Passed to
matplotlib.pyplot.imshow
.- radiological
bool
, default=False Invert x axis and R L labels to plot sections as a radiological view. If False (default), the left hemisphere is on the left of a coronal image. If True, left hemisphere is on the right.
- kwargsextra keyword arguments, optional
Extra keyword arguments ultimately passed to matplotlib.pyplot.imshow via
add_overlay
.
- Returns:
- display
OrthoSlicer
or None An instance of the OrthoSlicer class. If
output_file
is defined, None is returned.
Note
This is a low-level function. For most use cases, other plotting functions might be more appropriate and easier to use.
See also
plot_anat
To simply plot anatomical images
plot_epi
To simply plot raw EPI images
plot_roi
To simply plot max-prob atlases (3D images)
plot_prob_atlas
To simply plot probabilistic atlases (4D images)
nilearn.plotting
See API reference for other options
- display
Examples using nilearn.plotting.plot_img
¶
Basic nilearn example: manipulating and looking at data
Intro to GLM Analysis: a single-run, single-subject fMRI dataset
Searchlight analysis of face vs house recognition