mne.viz.plot_source_estimates

mne.viz.plot_source_estimates(stc, subject=None, surface='inflated', hemi='lh', colormap='auto', time_label='auto', smoothing_steps=10, transparent=True, alpha=1.0, time_viewer='auto', subjects_dir=None, figure=None, views='auto', colorbar=True, clim='auto', cortex='classic', size=800, background='black', foreground=None, initial_time=None, time_unit='s', backend='auto', spacing='oct6', title=None, show_traces='auto', src=None, volume_options=1.0, view_layout='vertical', add_data_kwargs=None, verbose=None)[source]

Plot SourceEstimate.

Parameters
stcSourceEstimate

The source estimates to plot.

subjectstr | None

The subject name corresponding to FreeSurfer environment variable SUBJECT. If None stc.subject will be used. If that is None, the environment will be used.

surfacestr

The type of surface (inflated, white etc.).

hemistr

Hemisphere id (ie ‘lh’, ‘rh’, ‘both’, or ‘split’). In the case of ‘both’, both hemispheres are shown in the same window. In the case of ‘split’ hemispheres are displayed side-by-side in different viewing panes.

colormapstr | np.ndarray of float, shape(n_colors, 3 | 4)

Name of colormap to use or a custom look up table. If array, must be (n x 3) or (n x 4) array for with RGB or RGBA values between 0 and 255. The default (‘auto’) uses ‘hot’ for one-sided data and ‘mne’ for two-sided data.

time_labelstr | callable() | None

Format of the time label (a format string, a function that maps floating point time values to strings, or None for no label). The default is 'auto', which will use time=%0.2f ms if there is more than one time point.

smoothing_stepsint

The amount of smoothing.

transparentbool | None

If True, use a linear transparency between fmin and fmid. None will choose automatically based on colormap type.

alphafloat

Alpha value to apply globally to the overlay. Has no effect with mpl backend.

time_viewerbool | str

Display time viewer GUI. Can also be ‘auto’, which will mean True for the PyVista backend and False otherwise.

Changed in version 0.20.0: “auto” mode added.

subjects_dirstr | None

The path to the freesurfer subjects reconstructions. It corresponds to Freesurfer environment variable SUBJECTS_DIR.

figureinstance of mayavi.core.api.Scene | instance of matplotlib.figure.Figure | list | int | None

If None, a new figure will be created. If multiple views or a split view is requested, this must be a list of the appropriate length. If int is provided it will be used to identify the Mayavi figure by it’s id or create a new figure with the given id. If an instance of matplotlib figure, mpl backend is used for plotting.

viewsstr | list

View to use. Can be any of:

['lateral', 'medial', 'rostral', 'caudal', 'dorsal', 'ventral',
 'frontal', 'parietal', 'axial', 'sagittal', 'coronal']

Three letter abbreviations (e.g., 'lat') are also supported. Using multiple views (list) is not supported for mpl backend.

When plotting a standard SourceEstimate (not volume, mixed, or vector) and using the PyVista backend, views='flat' is also supported to plot cortex as a flatmap.

Changed in version 0.21.0: Support for flatmaps.

colorbarbool

If True, display colorbar on scene.

climstr | dict

Colorbar properties specification. If ‘auto’, set clim automatically based on data percentiles. If dict, should contain:

kind‘value’ | ‘percent’

Flag to specify type of limits.

limslist | np.ndarray | tuple of float, 3 elements

Lower, middle, and upper bounds for colormap.

pos_limslist | np.ndarray | tuple of float, 3 elements

Lower, middle, and upper bound for colormap. Positive values will be mirrored directly across zero during colormap construction to obtain negative control points.

Note

Only one of lims or pos_lims should be provided. Only sequential colormaps should be used with lims, and only divergent colormaps should be used with pos_lims.

cortexstr or tuple

Specifies how binarized curvature values are rendered. Either the name of a preset PySurfer cortex colorscheme (one of ‘classic’, ‘bone’, ‘low_contrast’, or ‘high_contrast’), or the name of mayavi colormap, or a tuple with values (colormap, min, max, reverse) to fully specify the curvature colors. Has no effect with mpl backend.

sizefloat or tuple of float

The size of the window, in pixels. can be one number to specify a square window, or the (width, height) of a rectangular window. Has no effect with mpl backend.

backgroundmatplotlib color

Color of the background of the display window.

foregroundmatplotlib color | None

Color of the foreground of the display window. Has no effect with mpl backend. None will choose white or black based on the background color.

initial_timefloat | None

The time to display on the plot initially. None to display the first time sample (default).

time_unit‘s’ | ‘ms’

Whether time is represented in seconds (“s”, default) or milliseconds (“ms”).

backend‘auto’ | ‘mayavi’ | ‘matplotlib’

Which backend to use. If 'auto' (default), tries to plot with mayavi, but resorts to matplotlib if mayavi is not available.

New in version 0.15.0.

spacingstr

The spacing to use for the source space. Can be 'ico#' for a recursively subdivided icosahedron, 'oct#' for a recursively subdivided octahedron, or 'all' for all points. In general, you can speed up the plotting by selecting a sparser source space. Has no effect with mayavi backend. Defaults to ‘oct6’.

New in version 0.15.0.

titlestr | None

Title for the figure. If None, the subject name will be used.

New in version 0.17.0.

show_tracesbool | str | float

If True, enable interactive picking of a point on the surface of the brain and plot its time course. This feature is only available with the PyVista 3d backend, and requires time_viewer=True. Defaults to ‘auto’, which will use True if and only if time_viewer=True, the backend is PyVista, and there is more than one time point. If float (between zero and one), it specifies what proportion of the total window should be devoted to traces (True is equivalent to 0.25, i.e., it will occupy the bottom 1/4 of the figure).

New in version 0.20.0.

srcinstance of SourceSpaces | None

The source space corresponding to the source estimate. Only necessary if the STC is a volume or mixed source estimate.

volume_optionsfloat | dict | None

Options for volumetric source estimate plotting, with key/value pairs:

  • 'resolution'float | None

    Resolution (in mm) of volume rendering. Smaller (e.g., 1.) looks better at the cost of speed. None (default) uses the volume source space resolution, which is often something like 7 or 5 mm, without resampling.

  • 'blending'str

    Can be “mip” (default) for maximum intensity projection or “composite” for composite blending using alpha values.

  • 'alpha'float | None

    Alpha for the volumetric rendering. Defaults are 0.4 for vector source estimates and 1.0 for scalar source estimates.

  • 'surface_alpha'float | None

    Alpha for the surface enclosing the volume(s). None (default) will use half the volume alpha. Set to zero to avoid plotting the surface.

  • 'silhouette_alpha'float | None

    Alpha for a silhouette along the outside of the volume. None (default) will use 0.25 * surface_alpha.

  • 'silhouette_linewidth'float

    The line width to use for the silhouette. Default is 2.

A float input (default 1.) or None will be used for the 'resolution' entry.

view_layoutstr

Can be “vertical” (default) or “horizontal”. When using “horizontal” mode, the PyVista backend must be used and hemi cannot be “split”.

add_data_kwargsdict | None

Additional arguments to brain.add_data (e.g., dict(time_label_size=10)).

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns
figureinstance of surfer.Brain | matplotlib.figure.Figure

An instance of surfer.Brain from PySurfer or matplotlib figure.

Notes

Flatmaps are available by default for fsaverage but not for other subjects reconstructed by FreeSurfer. We recommend using mne.compute_source_morph() to morph source estimates to fsaverage for flatmap plotting. If you want to construct your own flatmap for a given subject, these links might help:

Examples using mne.viz.plot_source_estimates