mne.viz.plot_sparse_source_estimates(src, stcs, colors=None, linewidth=2, fontsize=18, bgcolor=(0.05, 0, 0.1), opacity=0.2, brain_color=(0.7, 0.7, 0.7), show=True, high_resolution=False, fig_name=None, fig_number=None, labels=None, modes=('cone', 'sphere'), scale_factors=(1, 0.6), verbose=None, **kwargs)[source]

Plot source estimates obtained with sparse solver.

Active dipoles are represented in a “Glass” brain. If the same source is active in multiple source estimates it is displayed with a sphere otherwise with a cone in 3D.

src : dict

The source space.

stcs : instance of SourceEstimate or list of instances of SourceEstimate

The source estimates (up to 3).

colors : list

List of colors

linewidth : int

Line width in 2D plot.

fontsize : int

Font size.

bgcolor : tuple of length 3

Background color in 3D.

opacity : float in [0, 1]

Opacity of brain mesh.

brain_color : tuple of length 3

Brain color.

show : bool

Show figures if True.

high_resolution : bool

If True, plot on the original (non-downsampled) cortical mesh.

fig_name :

Mayavi figure name.

fig_number :

Matplotlib figure number.

labels : ndarray or list of ndarrays

Labels to show sources in clusters. Sources with the same label and the waveforms within each cluster are presented in the same color. labels should be a list of ndarrays when stcs is a list ie. one label for each stc.

modes : list

Should be a list, with each entry being 'cone' or 'sphere' to specify how the dipoles should be shown.

scale_factors : list

List of floating point scale factors for the markers.

verbose : bool, str, int, or None

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

**kwargs : kwargs

Keyword arguments to pass to mlab.triangular_mesh.

surface : instance of mlab Surface

The triangular mesh surface.