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_surf_stat_map#

nilearn.plotting.plot_surf_stat_map(surf_mesh, stat_map, bg_map=None, hemi='left', view='lateral', engine='matplotlib', threshold=None, alpha=None, vmin=None, vmax=None, cmap='cold_hot', colorbar=True, symmetric_cbar='auto', cbar_tick_format='auto', bg_on_data=False, darkness=0.7, title=None, title_font_size=18, output_file=None, axes=None, figure=None, avg_method=None, **kwargs)[source]#

Plot a stats map on a surface mesh with optional background.

New in version 0.3.

Parameters:
surf_meshstr or list of two numpy.ndarray or Mesh

Surface mesh geometry, can be a file (valid formats are .gii or Freesurfer specific files such as .orig, .pial, .sphere, .white, .inflated) or a list of two Numpy arrays, the first containing the x-y-z coordinates of the mesh vertices, the second containing the indices (into coords) of the mesh faces, or a Mesh object with “coordinates” and “faces” attributes.

stat_mapstr or numpy.ndarray

Statistical map to be displayed on the surface mesh, can be a file (valid formats are .gii, .mgz, or Freesurfer specific files such as .thickness, .area, .curv, .sulc, .annot, .label) or a Numpy array with a value for each vertex of the surf_mesh.

bg_mapstr or numpy.ndarray, optional

Background image to be plotted on the mesh underneath the stat_map in greyscale, most likely a sulcal depth map for realistic shading. If the map contains values outside [0, 1], it will be rescaled such that all values are in [0, 1]. Otherwise, it will not be modified.

hemi{“left”, “right”}, default=”left”

Hemisphere to display.

viewstr, or a pair of float or int, default=”lateral”

If a string, must be in {“lateral”, “medial”, “dorsal”, “ventral”, “anterior”, “posterior”}. If a sequence, must be a pair (elev, azim) of float or int angles in degrees that will manually set a custom view. E.g., view=[270.0, 90] or view=(0, -180.0). View of the surface that is rendered.

engine{‘matplotlib’, ‘plotly’}, default=’matplotlib’

New in version 0.9.0.

Selects which plotting engine will be used by plot_surf_stat_map. Currently, only matplotlib and plotly are supported.

Note

To use the plotly engine you need to have plotly installed.

Note

To be able to save figures to disk with the plotly engine you need to have kaleido installed.

Warning

The plotly engine is new and experimental. Please report bugs that you may encounter.

thresholda number or None, 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.

cmapmatplotlib.colors.Colormap, or str, optional

The colormap to use. Either a string which is a name of a matplotlib colormap, or a matplotlib colormap object.

cbar_tick_formatstr, optional

Controls how to format the tick labels of the colorbar. Ex: use “%%.2g” to display using scientific notation. Default=”auto” which will select:

  • ‘%.2g’ (scientific notation) with matplotlib engine.

  • ‘.1f’ (rounded floats) with plotly engine.

New in version 0.7.1.

colorbarbool, optional

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

Note

This function uses a symmetric colorbar for the statistical map.

Default=True.

alphafloat or ‘auto’ or None, default=None

Alpha level of the mesh (not the stat_map). Will default to "auto" if None is passed. If ‘auto’ is chosen, alpha will default to .5 when no bg_map is passed and to 1 if a bg_map is passed.

Note

This option is currently only implemented for the matplotlib engine.

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.

symmetric_cbarbool, 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.

bg_on_databool, default=False

If True and a bg_map is specified, the surf_data data is multiplied by the background image, so that e.g. sulcal depth is jointly visible with surf_data. Otherwise, the background image will only be visible where there is no surface data (either because surf_data contains nans or because is was thresholded).

Note

This non-uniformly changes the surf_data values according to e.g the sulcal depth.

darknessfloat between 0 and 1, optional

Specifying the darkness of the background image:

  • 1 indicates that the original values of the background are used

  • 0.5 indicates that the background values are reduced by half before being applied.

Default=1.

Note

This option is currently only implemented for the matplotlib engine.

titlestr, or None, default=None

The title displayed on the figure.

title_font_sizeint, default=18

Size of the title font (only implemented for the plotly engine).

New in version 0.9.0.

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.

axesinstance of matplotlib axes, None, optional

The axes instance to plot to. The projection must be ‘3d’ (e.g., figure, axes = plt.subplots(subplot_kw={‘projection’: ‘3d’}), where axes should be passed.). If None, a new axes is created.

Note

This option is currently only implemented for the matplotlib engine.

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

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

Note

This option is currently only implemented for the matplotlib engine.

avg_method{“mean”, “median”, “min”, “max”, custom function, None}, default=None

How to average vertex values to derive the face value:

  • mean: results in smooth boundaries

  • median: results in sharp boundaries

  • min or max: for sparse matrices

  • custom function: You can also pass a custom function which will be executed though numpy.apply_along_axis. Here is an example of a custom function:

    def custom_function(vertices):
    return vertices[0] * vertices[1] * vertices[2]

Note

This option is currently only implemented for the matplotlib engine.

When using matplolib as engine, avg_method will default to "mean" if None is passed.

New in version 0.10.3dev.

kwargsdict, optional

Keyword arguments passed to nilearn.plotting.plot_surf.

See also

nilearn.datasets.fetch_surf_fsaverage

For surface data object to be used as background map for this plotting function.

nilearn.plotting.plot_surf

For brain surface visualization.

nilearn.surface.vol_to_surf

For info on the generation of surfaces.

Examples using nilearn.plotting.plot_surf_stat_map#

Example of surface-based first-level analysis

Example of surface-based first-level analysis

Surface-based dataset first and second level analysis of a dataset

Surface-based dataset first and second level analysis of a dataset