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:
- src
dict
The source space.
- stcsinstance of
SourceEstimate
orlist
of instances ofSourceEstimate
The source estimates.
- 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
str
PyVista figure name.
- fig_number
int
Matplotlib figure number.
- labels
ndarray
orlist
ofndarray
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. The pivot for the glyphs in'cone'
mode is always the tail whereas the pivot in'sphere'
mode is the center.- scale_factors
list
List of floating point scale factors for the markers.
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.- **kwargskwargs
Keyword arguments to pass to renderer.mesh.
- src
- Returns:
- surfaceinstance of
Figure3D
The 3D figure containing the triangular mesh surface.
- surfaceinstance of
Examples using mne.viz.plot_sparse_source_estimates
#
Generate simulated evoked data
Source localization with a custom inverse solver
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
Compute MxNE with time-frequency sparse prior