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_stat_map#
- nilearn.plotting.plot_stat_map(stat_map_img, bg_img=<MNI152Template>, cut_coords=None, output_file=None, display_mode='ortho', colorbar=True, cbar_tick_format='%.2g', figure=None, axes=None, title=None, threshold=1e-06, annotate=True, draw_cross=True, black_bg='auto', cmap=<matplotlib.colors.LinearSegmentedColormap object>, symmetric_cbar='auto', dim='auto', vmin=None, vmax=None, radiological=False, resampling_interpolation='continuous', **kwargs)[source]#
Plot cuts of an ROI/mask image.
By default 3 cuts: Frontal, Axial, and Lateral.
- Parameters:
- stat_map_imgNiimg-like object
See Input and output: neuroimaging data representation. The statistical map image
- bg_imgNiimg-like object, optional
See Input and output: neuroimaging data representation. The background image to plot on top of. If nothing is specified, the MNI152 template will be used. To turn off background image, just pass “bg_img=None”. Default=MNI152TEMPLATE.
- 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
- colorbar
bool
, optional If True, display a colorbar on the right of the plots. Default=True.
- 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.
- 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. Default=1e-6.
- annotate
bool
, default=True If annotate is True, positions and left/right annotation are added to the plot.
- 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=’auto’.- cmap
matplotlib.colors.Colormap
, orstr
, optional The colormap to use. Either a string which is a name of a matplotlib colormap, or a matplotlib colormap object.
Note
The colormap must be symmetrical.
Default=`plt.cm.cold_hot`.
- symmetric_cbar
bool
, or “auto”, default=”auto” Specifies whether the colorbar and colormap should range from -vmax to vmax (or from vmin to -vmin if -vmin is greater than vmax) or from vmin to vmax. Setting to “auto” (the default) will select the former if either vmin or vmax is None and the image has both positive and negative values.
- dim
float
, or “auto”, 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 span is between -2 and 2 (-2 = increase contrast; 2 = decrease contrast), but larger values can be used for a more pronounced effect. 0 means no dimming. Default=’auto’.
- 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
.- 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’.
- 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.
- kwargs: extra 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.
- display
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.
Examples using nilearn.plotting.plot_stat_map
#
Voxel-Based Morphometry on Oasis dataset
Different classifiers in decoding the Haxby dataset
Encoding models for visual stimuli from Miyawaki et al. 2008
Producing single subject maps of seed-to-voxel correlation
Deriving spatial maps from group fMRI data using ICA and Dictionary Learning
Regions extraction using dictionary learning and functional connectomes
Default Mode Network extraction of ADHD dataset
Analysis of an fMRI dataset with a Finite Impule Response (FIR) model
Single-subject data (two runs) in native space
Predicted time series and residuals
Simple example of two-runs fMRI model fitting
Understanding parameters of the first-level model
Second-level fMRI model: true positive proportion in clusters
Statistical testing of a second-level analysis
Voxel-Based Morphometry on OASIS dataset
Example of generic design in second-level models
Resample an image to a template
Regions Extraction of Default Mode Networks using Smith Atlas
Simple example of NiftiMasker use
Region Extraction using a t-statistical map (3D)
Computing a Region of Interest (ROI) mask manually
Multivariate decompositions: Independent component analysis of fMRI
Massively univariate analysis of a calculation task from the Localizer dataset
NeuroVault meta-analysis of stop-go paradigm studies
NeuroVault cross-study ICA maps
Massively univariate analysis of face vs house recognition
Advanced decoding using scikit learn
Beta-Series Modeling for Task-Based Functional Connectivity and Decoding