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_glass_brain(stat_map_img, output_file=None, display_mode='ortho', colorbar=False, figure=None, axes=None, title=None, threshold='auto', annotate=True, black_bg=False, cmap=None, alpha=0.7, vmin=None, vmax=None, plot_abs=True, symmetric_cbar='auto', resampling_interpolation='continuous', **kwargs)¶
Plot 2d projections of an ROI/mask image (by default 3 projections: Frontal, Axial, and Lateral). The brain glass schematics are added on top of the image.
The plotted image should be in MNI space for this function to work properly.
Only glass brain can be plotted by switching stat_map_img to None.
- stat_map_imgNiimg-like object
See http://nilearn.github.io/manipulating_images/input_output.html The statistical map image. It needs to be in MNI space in order to align with the brain schematics.
- 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_modestring, optional
Choose the direction of the cuts: ‘x’ - sagittal, ‘y’ - coronal, ‘z’ - axial, ‘l’ - sagittal left hemisphere only, ‘r’ - sagittal right hemisphere only, ‘ortho’ - three cuts are performed in orthogonal directions. Possible values are: ‘ortho’, ‘x’, ‘y’, ‘z’, ‘xz’, ‘yx’, ‘yz’, ‘l’, ‘r’, ‘lr’, ‘lzr’, ‘lyr’, ‘lzry’, ‘lyrz’. Default=’ortho’.
- colorbarboolean, optional
If True, display a colorbar on the right of the plots. Default=False.
- 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 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=’auto’.
- annotateboolean, optional
If annotate is True, positions and left/right annotation are added to the plot. Default=True.
- 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. Default=False.
- cmapmatplotlib colormap, optional
The colormap for specified image
- alphafloat between 0 and 1, optional
Alpha transparency for the brain schematics. Default=0.7.
- vminfloat, optional
Lower bound for plotting, passed to matplotlib.pyplot.imshow.
- vmaxfloat, optional
Upper bound for plotting, passed to matplotlib.pyplot.imshow.
- plot_absboolean, optional
If set to True (default) maximum intensity projection of the absolute value will be used (rendering positive and negative values in the same manner). If set to false the sign of the maximum intensity will be represented with different colors. See http://nilearn.github.io/auto_examples/01_plotting/plot_demo_glass_brain_extensive.html for examples. Default=True.
- symmetric_cbarboolean or ‘auto’, optional
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. Default=’auto’.
- resampling_interpolationstr, optional
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. Default=’continuous’.
Arrays should be passed in numpy convention: (x, y, z) ordered.