mne.viz.plot_sparse_source_estimates#

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.

Parameters:

srcdict: The source space.
stcsinstance of SourceEstimate or list of instances of SourceEstimate: The source estimates.
colorslist: List of colors.
linewidthint: Line width in 2D plot.
fontsizeint: Font size.
bgcolortuple of length 3: Background color in 3D.
opacityfloat in [0, 1]: Opacity of brain mesh.
brain_colortuple of length 3: Brain color.
showbool: Show figures if True.
high_resolutionbool: If True, plot on the original (non-downsampled) cortical mesh.
fig_namestr: PyVista figure name.
fig_numberint: Matplotlib figure number.
labelsndarray or list of ndarray: 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.
modeslist: Should be a list, with each entry being 'cone' or 'sphere' to specify how the dipoles should be shown. The pivot for the glyphs in 'cone' mode is always the tail whereas the pivot in 'sphere' mode is the center.
scale_factorslist: List of floating point scale factors for the markers.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.
**kwargskwargs: Keyword arguments to pass to renderer.mesh.

Returns: