mne.viz.Brain#
- class mne.viz.Brain(subject=None, hemi='both', surf='pial', title=None, cortex='classic', alpha=1.0, size=800, background='black', foreground=None, figure=None, subjects_dir=None, views='auto', *, offset='auto', show_toolbar=None, offscreen=False, interaction='trackball', units='mm', view_layout='vertical', silhouette=False, theme=None, show=True, block=False, subject_id=None)[source]#
Class for visualizing a brain.
Warning
The API for this class is not currently complete. We suggest using
mne.viz.plot_source_estimates()
with the PyVista backend enabled to obtain aBrain
instance.- Parameters:
- subject
str
Subject name in Freesurfer subjects dir.
Changed in version 1.2: This parameter was renamed from
subject_id
tosubject
.- hemi
str
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.
- surf
str
FreeSurfer surface mesh name (ie ‘white’, ‘inflated’, etc.).
- title
str
Title for the window.
- cortex
str
,list
,dict
Specifies how the cortical surface is rendered. Options:
- The name of one of the preset cortex styles:
'classic'
(default),'high_contrast'
,'low_contrast'
, or'bone'
.
- A single color-like argument to render the cortex as a single
color, e.g.
'red'
or(0.1, 0.4, 1.)
.
- A list of two color-like used to render binarized curvature
values for gyral (first) and sulcal (second). regions, e.g.,
['red', 'blue']
or[(1, 0, 0), (0, 0, 1)]
.
- A dict containing keys
'vmin', 'vmax', 'colormap'
with values used to render the binarized curvature (where 0 is gyral, 1 is sulcal).
- A dict containing keys
Changed in version 0.24: Add support for non-string arguments.
- alpha
float
in [0, 1] Alpha level to control opacity of the cortical surface.
- size
int
| array_like, shape (2,) The size of the window, in pixels. can be one number to specify a square window, or a length-2 sequence to specify (width, height).
- background
tuple
(int
,int
,int
) The color definition of the background: (red, green, blue).
- foregroundmatplotlib color
Color of the foreground (will be used for colorbars and text). None (default) will use black or white depending on the value of
background
.- figure
list
ofFigure
|None
If None (default), a new window will be created with the appropriate views.
- subjects_dir
str
|None
If not None, this directory will be used as the subjects directory instead of the value set using the SUBJECTS_DIR environment variable.
- views
str
|list
View to use. Using multiple views (list) is not supported for mpl backend. See
Brain.show_view
for valid string options.- offset
bool
|str
If True, shifts the right- or left-most x coordinate of the left and right surfaces, respectively, to be at zero. This is useful for viewing inflated surface where hemispheres typically overlap. Can be “auto” (default) use True with inflated surfaces and False otherwise (Default: ‘auto’). Only used when
hemi='both'
.Changed in version 0.23: Default changed to “auto”.
- show_toolbar
bool
If True, toolbars will be shown for each view.
- offscreen
bool
If True, rendering will be done offscreen (not shown). Useful mostly for generating images or screenshots, but can be buggy. Use at your own risk.
- interaction
str
Can be “trackball” (default) or “terrain”, i.e. a turntable-style camera.
- units
str
Can be ‘m’ or ‘mm’ (default).
- view_layout
str
Can be “vertical” (default) or “horizontal”. When using “horizontal” mode, the PyVista backend must be used and hemi cannot be “split”.
- silhouette
dict
|bool
As a dict, it contains the
color
,linewidth
,alpha
opacity anddecimate
(level of decimation between 0 and 1 or None) of the brain’s silhouette to display. If True, the default values are used and if False, no silhouette will be displayed. Defaults to False.- theme
str
| path-like Can be “auto”, “light”, or “dark” or a path-like to a custom stylesheet. For Dark-Mode and automatic Dark-Mode-Detection,
qdarkstyle
and darkdetect, respectively, are required. If None (default), the config option MNE_3D_OPTION_THEME will be used, defaulting to “auto” if it’s not found.- show
bool
Display the window as soon as it is ready. Defaults to True.
- block
bool
If True, start the Qt application event loop. Default to False.
- subject_id
str
|None
Deprecated, use
subject
instead.
- subject
Notes
This table shows the capabilities of each Brain backend (”✓” for full support, and “-” for partial support):
3D function:
surfer.Brain
mne.viz.Brain
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
data
✓
✓
foci
✓
labels
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
TimeViewer
✓
✓
✓
✓
view_layout
✓
flatmaps
✓
vertex picking
✓
label picking
✓
- Attributes:
Methods
add_annotation
(annot[, borders, alpha, ...])Add an annotation file.
add_data
(array[, fmin, fmid, fmax, thresh, ...])Display data from a numpy array on the surface or volume.
add_dipole
(dipole, trans[, colors, alpha, ...])Add a quiver to render positions of dipoles.
add_foci
(coords[, coords_as_verts, ...])Add spherical foci, possibly mapping to displayed surf.
add_forward
(fwd, trans[, alpha, scale])Add a quiver to render positions of dipoles.
add_head
([dense, color, alpha])Add a mesh to render the outer head surface.
add_label
(label[, color, alpha, ...])Add an ROI label to the image.
add_sensors
(info, trans[, meg, eeg, fnirs, ...])Add mesh objects to represent sensor positions.
add_skull
([outer, color, alpha])Add a mesh to render the skull surface.
add_text
(x, y, text[, name, color, opacity, ...])Add a text to the visualization.
add_volume_labels
([aseg, labels, colors, ...])Add labels to the rendering from an anatomical segmentation.
Detect automatically fitting scaling parameters.
Clear the picking glyphs.
close
()Close all figures and cleanup data structure.
Return the vertices of the picked points.
get_view
([row, col])Get the camera orientation for a given subplot display.
help
()Display the help window.
plot_time_course
(hemi, vertex_id, color[, ...])Plot the vertex time course.
plot_time_line
([update])Add the time line to the MPL widget.
Remove all annotations from the image.
Remove rendered data from the mesh.
Remove dipole objects from the rendered scene.
Remove forward sources from the rendered scene.
Remove head objects from the rendered scene.
Remove all the ROI labels from the image.
remove_sensors
([kind])Remove sensors from the rendered scene.
Remove skull objects from the rendered scene.
remove_text
([name])Remove text from the rendered scene.
Remove the volume labels from the rendered scene.
reset
()Reset view and time step.
Reset the camera.
Restore original scaling parameters.
save_image
([filename, mode])Save view from all panels to disk.
save_movie
([filename, time_dilation, tmin, ...])Save a movie (for data with a time axis).
screenshot
([mode, time_viewer])Generate a screenshot of current view.
set_data_smoothing
(n_steps)Set the number of smoothing steps.
set_playback_speed
(speed)Set the time playback speed.
set_time
(time)Set the time to display (in seconds).
set_time_interpolation
(interpolation)Set the interpolation mode.
set_time_point
(time_idx)Set the time point shown (can be a float to interpolate).
setup_time_viewer
([time_viewer, show_traces])Configure the time viewer parameters.
show
()Display the window.
show_view
([view, roll, distance, row, col, ...])Orient camera to display view.
toggle_interface
([value])Toggle the interface.
toggle_playback
([value])Toggle time playback.
update_lut
([fmin, fmid, fmax, alpha])Update color map.
- add_annotation(annot, borders=True, alpha=1, hemi=None, remove_existing=True, color=None)[source]#
Add an annotation file.
- Parameters:
- annot
str
|tuple
Either path to annotation file or annotation name. Alternatively, the annotation can be specified as a
(labels, ctab)
tuple per hemisphere, i.e.annot=(labels, ctab)
for a single hemisphere orannot=((lh_labels, lh_ctab), (rh_labels, rh_ctab))
for both hemispheres.labels
andctab
should be arrays as returned bynibabel.freesurfer.io.read_annot()
.- borders
bool
|int
Show only label borders. If int, specify the number of steps (away from the true border) along the cortical mesh to include as part of the border definition.
- alpha
float
in [0, 1] Alpha level to control opacity. Default is 1.
- hemi
str
|None
If None, it is assumed to belong to the hemisphere being shown. If two hemispheres are being shown, data must exist for both hemispheres.
- remove_existing
bool
If True (default), remove old annotations.
- colormatplotlib-style color
code
If used, show all annotations in the same (specified) color. Probably useful only when showing annotation borders.
- annot
Examples using
add_annotation
:FreeSurfer MRI reconstructionVisualize source time courses (stcs)
Visualize source time courses (stcs)Plot a cortical parcellation
- add_data(array, fmin=None, fmid=None, fmax=None, thresh=None, center=None, transparent=False, colormap='auto', alpha=1, vertices=None, smoothing_steps=None, time=None, time_label='auto', colorbar=True, hemi=None, remove_existing=None, time_label_size=None, initial_time=None, scale_factor=None, vector_alpha=None, clim=None, src=None, volume_options=0.4, colorbar_kwargs=None, verbose=None)[source]#
Display data from a numpy array on the surface or volume.
This provides a similar interface to
surfer.Brain.add_overlay()
, but it displays it with a single colormap. It offers more flexibility over the colormap, and provides a way to display four-dimensional data (i.e., a timecourse) or five-dimensional data (i.e., a vector-valued timecourse).Note
fmin
sets the low end of the colormap, and is separate from thresh (this is a different convention fromsurfer.Brain.add_overlay()
).- Parameters:
- array
numpy
array
, shape (n_vertices[, 3][, n_times]) Data array. For the data to be understood as vector-valued (3 values per vertex corresponding to X/Y/Z surface RAS), then
array
must be have all 3 dimensions. If vectors with no time dimension are desired, consider using a singleton (e.g.,np.newaxis
) to create a “time” dimension and passtime_label=None
(vector values are not supported).- fmin
float
Minimum value in colormap (uses real fmin if None).
- fmid
float
Intermediate value in colormap (fmid between fmin and fmax if None).
- fmax
float
Maximum value in colormap (uses real max if None).
- thresh
None
orfloat
Not supported yet. If not None, values below thresh will not be visible.
- center
float
orNone
If not None, center of a divergent colormap, changes the meaning of fmin, fmax and fmid.
- transparent
bool
|None
If True: use a linear transparency between fmin and fmid and make values below fmin fully transparent (symmetrically for divergent colormaps). None will choose automatically based on colormap type.
- colormap
str
,list
of color, orarray
Name of matplotlib colormap to use, a list of matplotlib colors, or a custom look up table (an n x 4 array coded with RBGA values between 0 and 255), the default “auto” chooses a default divergent colormap, if “center” is given (currently “icefire”), otherwise a default sequential colormap (currently “rocket”).
- alpha
float
in [0, 1] Alpha level to control opacity of the overlay.
- vertices
numpy
array
Vertices for which the data is defined (needed if
len(data) < nvtx
).- smoothing_steps
int
orNone
Number of smoothing steps (smoothing is used if len(data) < nvtx) The value ‘nearest’ can be used too. None (default) will use as many as necessary to fill the surface.
- time
numpy
array
Time points in the data array (if data is 2D or 3D).
- time_label
str
|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 usetime=%0.2f ms
if there is more than one time point.- colorbar
bool
Whether to add a colorbar to the figure. Can also be a tuple to give the (row, col) index of where to put the colorbar.
- hemi
str
|None
If None, it is assumed to belong to the hemisphere being shown. If two hemispheres are being shown, an error will be thrown.
- remove_existing
bool
Not supported yet. Remove surface added by previous “add_data” call. Useful for conserving memory when displaying different data in a loop.
- time_label_size
int
Font size of the time label (default 14).
- initial_time
float
|None
Time initially shown in the plot.
None
to use the first time sample (default).- scale_factor
float
|None
(default) The scale factor to use when displaying glyphs for vector-valued data.
- vector_alpha
float
|None
Alpha level to control opacity of the arrows. Only used for vector-valued data. If None (default),
alpha
is used.- clim
dict
Original clim arguments.
- 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_options
float
|dict
|None
Options for volumetric source estimate plotting, with key/value pairs:
'resolution'
float | NoneResolution (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'
strCan be “mip” (default) for maximum intensity projection or “composite” for composite blending using alpha values.
'alpha'
float | NoneAlpha for the volumetric rendering. Defaults are 0.4 for vector source estimates and 1.0 for scalar source estimates.
'surface_alpha'
float | NoneAlpha 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 | NoneAlpha for a silhouette along the outside of the volume. None (default) will use
0.25 * surface_alpha
.
'silhouette_linewidth'
floatThe line width to use for the silhouette. Default is 2.
A float input (default 1.) or None will be used for the
'resolution'
entry.- colorbar_kwargs
dict
|None
Options to pass to
pyvista.Plotter.add_scalar_bar()
(e.g.,dict(title_font_size=10)
).- 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.
- array
Notes
If the data is defined for a subset of vertices (specified by the “vertices” parameter), a smoothing method is used to interpolate the data onto the high resolution surface. If the data is defined for subsampled version of the surface, smoothing_steps can be set to None, in which case only as many smoothing steps are applied until the whole surface is filled with non-zeros.
Due to a VTK alpha rendering bug,
vector_alpha
is clamped to be strictly < 1.Examples using
add_data
:Plotting with ``mne.viz.Brain``
- add_dipole(dipole, trans, colors='red', alpha=1, scales=None)[source]#
Add a quiver to render positions of dipoles.
- Parameters:
- dipoleinstance of
Dipole
Dipole object containing position, orientation and amplitude of one or more dipoles or in the forward solution.
- trans
str
|dict
| instance ofTransform
If str, the path to the head<->MRI transform
*-trans.fif
file produced during coregistration. Can also be'fsaverage'
to use the built-in fsaverage transformation.- colors
list
| matplotlib-style color |None
A single color or list of anything matplotlib accepts: string, RGB, hex, etc. Default red.
- alpha
float
in [0, 1] Alpha level to control opacity. Default 1.
- scales
list
|float
|None
The size of the arrow representing the dipole in
mne.viz.Brain
units. Default 5mm.
- dipoleinstance of
Notes
New in version 1.0.
Examples using
add_dipole
:Plotting with ``mne.viz.Brain``
- add_foci(coords, coords_as_verts=False, map_surface=None, scale_factor=1, color='white', alpha=1, name=None, hemi=None, resolution=50)[source]#
Add spherical foci, possibly mapping to displayed surf.
The foci spheres can be displayed at the coordinates given, or mapped through a surface geometry. In other words, coordinates from a volume-based analysis in MNI space can be displayed on an inflated average surface by finding the closest vertex on the white surface and mapping to that vertex on the inflated mesh.
- Parameters:
- coords
ndarray
, shape (n_coords, 3) Coordinates in stereotaxic space (default) or array of vertex ids (with
coord_as_verts=True
).- coords_as_verts
bool
Whether the coords parameter should be interpreted as vertex ids.
- map_surface
str
|None
Surface to project the coordinates to, or None to use raw coords. When set to a surface, each foci is positioned at the closest vertex in the mesh.
- scale_factor
float
Controls the size of the foci spheres (relative to 1cm).
- colorcolor
A list of anything matplotlib accepts: string, RGB, hex, etc.
- alpha
float
in [0, 1] Alpha level to control opacity. Default is 1.
- name
str
Internal name to use.
- hemi
str
|None
If None, it is assumed to belong to the hemisphere being shown. If two hemispheres are being shown, an error will be thrown.
- resolution
int
The resolution of the spheres.
- coords
Examples using
add_foci
:How MNE uses FreeSurfer’s outputs
How MNE uses FreeSurfer's outputsThe SourceEstimate data structure
The SourceEstimate data structureSource localization with MNE, dSPM, sLORETA, and eLORETA
Source localization with MNE, dSPM, sLORETA, and eLORETADICS for power mappingPlot point-spread functions (PSFs) and cross-talk functions (CTFs)
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)Compute cross-talk functions for LCMV beamformers
Compute cross-talk functions for LCMV beamformersPlot point-spread functions (PSFs) for a volume
Plot point-spread functions (PSFs) for a volume
- add_forward(fwd, trans, alpha=1, scale=None)[source]#
Add a quiver to render positions of dipoles.
- Parameters:
- fwdinstance of
Forward
The forward solution. If present, the orientations of the dipoles present in the forward solution are displayed.
- trans
str
|dict
| instance ofTransform
If str, the path to the head<->MRI transform
*-trans.fif
file produced during coregistration. Can also be'fsaverage'
to use the built-in fsaverage transformation.- alpha
float
in [0, 1] Alpha level to control opacity. Default 1.
- scale
None
|float
The size of the arrow representing the dipoles in
mne.viz.Brain
units. Default 1.5mm.
- fwdinstance of
Notes
New in version 1.0.
- add_head(dense=True, color='gray', alpha=0.5)[source]#
Add a mesh to render the outer head surface.
- Parameters:
Notes
New in version 0.24.
Examples using
add_head
:Importing data from fNIRS devices
Importing data from fNIRS devicesPlotting with ``mne.viz.Brain``
- add_label(label, color=None, alpha=1, scalar_thresh=None, borders=False, hemi=None, subdir=None, reset_camera=True)[source]#
Add an ROI label to the image.
- Parameters:
- label
str
| instance ofLabel
Label filepath or name. Can also be an instance of an object with attributes “hemi”, “vertices”, “name”, and optionally “color” and “values” (if scalar_thresh is not None).
- colormatplotlib-style color |
None
Anything matplotlib accepts: string, RGB, hex, etc. (default “crimson”).
- alpha
float
in [0, 1] Alpha level to control opacity.
- scalar_thresh
None
|float
Threshold the label ids using this value in the label file’s scalar field (i.e. label only vertices with scalar >= thresh).
- borders
bool
|int
Show only label borders. If int, specify the number of steps (away from the true border) along the cortical mesh to include as part of the border definition.
- hemi
str
|None
If None, it is assumed to belong to the hemisphere being shown.
- subdir
None
|str
If a label is specified as name, subdir can be used to indicate that the label file is in a sub-directory of the subject’s label directory rather than in the label directory itself (e.g. for
$SUBJECTS_DIR/$SUBJECT/label/aparc/lh.cuneus.label
brain.add_label('cuneus', subdir='aparc')
).- reset_camera
bool
If True, reset the camera view after adding the label. Defaults to True.
- label
Notes
To remove previously added labels, run Brain.remove_labels().
Examples using
add_label
:Plotting with ``mne.viz.Brain``Plot a cortical parcellationCompute Power Spectral Density of inverse solution from single epochs
Compute Power Spectral Density of inverse solution from single epochsGenerate a functional label from source estimates
Generate a functional label from source estimatesCompute MxNE with time-frequency sparse prior
Compute MxNE with time-frequency sparse prior
- add_sensors(info, trans, meg=None, eeg='original', fnirs=True, ecog=True, seeg=True, dbs=True, verbose=None)[source]#
Add mesh objects to represent sensor positions.
- Parameters:
- info
mne.Info
The
mne.Info
object with information about the sensors and methods of measurement.- trans
str
|dict
| instance ofTransform
If str, the path to the head<->MRI transform
*-trans.fif
file produced during coregistration. Can also be'fsaverage'
to use the built-in fsaverage transformation.- meg
str
|list
|bool
|None
Can be “helmet”, “sensors” or “ref” to show the MEG helmet, sensors or reference sensors respectively, or a combination like
('helmet', 'sensors')
(same as None, default). True translates to('helmet', 'sensors', 'ref')
.- eeg
bool
|str
|list
String options are:
- “original” (default; equivalent to
True
) Shows EEG sensors using their digitized locations (after transformation to the chosen
coord_frame
)
- “original” (default; equivalent to
- “projected”
The EEG locations projected onto the scalp, as is done in forward modeling
Can also be a list of these options, or an empty list (
[]
, equivalent ofFalse
).- fnirs
str
|list
|bool
|None
Can be “channels”, “pairs”, “detectors”, and/or “sources” to show the fNIRS channel locations, optode locations, or line between source-detector pairs, or a combination like
('pairs', 'channels')
. True translates to('pairs',)
.- ecog
bool
If True (default), show ECoG sensors.
- seeg
bool
If True (default), show sEEG electrodes.
- dbs
bool
If True (default), show DBS (deep brain stimulation) electrodes.
- 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.
- info
Notes
New in version 0.24.
Examples using
add_sensors
:Importing data from fNIRS devices
Importing data from fNIRS devicesPreprocessing functional near-infrared spectroscopy (fNIRS) data
Preprocessing functional near-infrared spectroscopy (fNIRS) dataLocating intracranial electrode contacts
Locating intracranial electrode contactsPlotting with ``mne.viz.Brain``
- add_skull(outer=True, color='gray', alpha=0.5)[source]#
Add a mesh to render the skull surface.
- Parameters:
Notes
New in version 0.24.
- add_text(x, y, text, name=None, color=None, opacity=1.0, row=0, col=0, font_size=None, justification=None)[source]#
Add a text to the visualization.
- Parameters:
- x
float
X coordinate.
- y
float
Y coordinate.
- text
str
Text to add.
- name
str
Name of the text (text label can be updated using update_text()).
- color
tuple
Color of the text. Default is the foreground color set during initialization (default is black or white depending on the background color).
- opacity
float
Opacity of the text (default 1.0).
- row
int
|None
Row index of which brain to use. Default is the top row.
- col
int
|None
Column index of which brain to use. Default is the left-most column.
- font_size
float
|None
The font size to use.
- justification
str
|None
The text justification.
- x
Examples using
add_text
:The SourceEstimate data structure
The SourceEstimate data structureSource localization with MNE, dSPM, sLORETA, and eLORETA
Source localization with MNE, dSPM, sLORETA, and eLORETAComputing various MNE solutions
Computing various MNE solutionsDisplay sensitivity maps for EEG and MEG sensors
Display sensitivity maps for EEG and MEG sensorsMorph surface source estimateVisualize source leakage among labels using a circular graph
Visualize source leakage among labels using a circular graphPlot point-spread functions (PSFs) and cross-talk functions (CTFs)
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)Compute cross-talk functions for LCMV beamformers
Compute cross-talk functions for LCMV beamformersPlot point-spread functions (PSFs) for a volume
Plot point-spread functions (PSFs) for a volumeCompute spatial resolution metrics in source space
Compute spatial resolution metrics in source spaceCompute spatial resolution metrics to compare MEG with EEG+MEG
Compute spatial resolution metrics to compare MEG with EEG+MEG
- add_volume_labels(aseg='aparc+aseg', labels=None, colors=None, alpha=0.5, smooth=0.9, fill_hole_size=None, legend=None)[source]#
Add labels to the rendering from an anatomical segmentation.
- Parameters:
- aseg
str
The anatomical segmentation file. Default
aparc+aseg
. This may be any anatomical segmentation file in the mri subdirectory of the Freesurfer subject directory.- labels
list
Labeled regions of interest to plot. See
mne.get_montage_volume_labels()
for one way to determine regions of interest. Regions can also be chosen from the FreeSurfer LUT.- colors
list
| matplotlib-style color |None
A list of anything matplotlib accepts: string, RGB, hex, etc. (default FreeSurfer LUT colors).
- alpha
float
in [0, 1] Alpha level to control opacity.
- smooth
float
in [0, 1) The smoothing factor to be applied. Default 0 is no smoothing.
- fill_hole_size
int
|None
The size of holes to remove in the mesh in voxels. Default is None, no holes are removed. Warning, this dilates the boundaries of the surface by
fill_hole_size
number of voxels so use the minimal size.- legend
bool
|None
|dict
Add a legend displaying the names of the
labels
. Default (None) isTrue
if the number oflabels
is 10 or fewer. Can also be a dict ofkwargs
to pass topyvista.Plotter.add_legend()
.
- aseg
Notes
New in version 0.24.
Examples using
add_volume_labels
:Visualize source time courses (stcs)
Visualize source time courses (stcs)Working with sEEG data
- close()[source]#
Close all figures and cleanup data structure.
Examples using
close
:Make figures more publication ready
Make figures more publication ready
- property data#
Data used by time viewer and color bar widgets.
- get_view(row=0, col=0)[source]#
Get the camera orientation for a given subplot display.
- Parameters:
- Returns:
- roll
float
|None
The roll of the camera rendering the view in degrees.
- distance
float
|None
The distance from the camera rendering the view to the focalpoint in plot units (either m or mm).
- azimuth
float
The azimuthal angle of the camera rendering the view in degrees.
- elevation
float
The The zenith angle of the camera rendering the view in degrees.
- focalpoint
tuple
, shape (3,) |None
The focal point of the camera rendering the view: (x, y, z) in plot units (either m or mm).
- roll
- property interaction#
The interaction style.
- plot_time_line(update=True)[source]#
Add the time line to the MPL widget.
- Parameters:
- update
bool
Force an update of the plot. Defaults to True.
- update
- save_image(filename=None, mode='rgb')[source]#
Save view from all panels to disk.
Examples using
save_image
:Repeated measures ANOVA on source data with spatio-temporal clustering
Repeated measures ANOVA on source data with spatio-temporal clustering
- save_movie(filename=None, time_dilation=4.0, tmin=None, tmax=None, framerate=24, interpolation=None, codec=None, bitrate=None, callback=None, time_viewer=False, **kwargs)[source]#
Save a movie (for data with a time axis).
The movie is created through the
imageio
module. The format is determined by the extension, and additional options can be specified through keyword arguments that depend on the format, see imageio’s format page.Warning
This method assumes that time is specified in seconds when adding data. If time is specified in milliseconds this will result in movies 1000 times longer than expected.
- Parameters:
- filename
str
Path at which to save the movie. The extension determines the format (e.g.,
'*.mov'
,'*.gif'
, …; see theimageio
documentation for available formats).- time_dilation
float
Factor by which to stretch time (default 4). For example, an epoch from -100 to 600 ms lasts 700 ms. With
time_dilation=4
this would result in a 2.8 s long movie.- tmin
float
First time point to include (default: all data).
- tmax
float
Last time point to include (default: all data).
- framerate
float
Framerate of the movie (frames per second, default 24).
- interpolation
str
|None
Interpolation method (
scipy.interpolate.interp1d
parameter). Must be one of ‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, or ‘cubic’. If None, it uses the currentbrain.interpolation
, which defaults to'nearest'
. Defaults to None.- codec
str
|None
The codec to use.
- bitrate
float
|None
The bitrate to use.
- callback
callable()
|None
A function to call on each iteration. Useful for status message updates. It will be passed keyword arguments
frame
andn_frames
.- time_viewer
bool
If True, include time viewer traces. Only used if
time_viewer=True
andseparate_canvas=False
.- **kwargs
dict
Specify additional options for
imageio
.
- filename
- screenshot(mode='rgb', time_viewer=False)[source]#
Generate a screenshot of current view.
- Parameters:
- Returns:
- screenshot
array
Image pixel values.
- screenshot
Examples using
screenshot
:Plotting with ``mne.viz.Brain``Make figures more publication ready
Make figures more publication ready
- set_data_smoothing(n_steps)[source]#
Set the number of smoothing steps.
- Parameters:
- n_steps
int
Number of smoothing steps.
- n_steps
- set_playback_speed(speed)[source]#
Set the time playback speed.
- Parameters:
- speed
float
The speed of the playback.
- speed
- set_time(time)[source]#
Set the time to display (in seconds).
- Parameters:
- time
float
The time to show, in seconds.
- time
- set_time_interpolation(interpolation)[source]#
Set the interpolation mode.
- Parameters:
- interpolation
str
|None
Interpolation method (
scipy.interpolate.interp1d
parameter). Must be one of ‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, or ‘cubic’.
- interpolation
- setup_time_viewer(time_viewer=True, show_traces=True)[source]#
Configure the time viewer parameters.
- Parameters:
Notes
The keyboard shortcuts are the following:
‘?’: Display help window ‘i’: Toggle interface ‘s’: Apply auto-scaling ‘r’: Restore original clim ‘c’: Clear all traces ‘n’: Shift the time forward by the playback speed ‘b’: Shift the time backward by the playback speed ‘Space’: Start/Pause playback ‘Up’: Decrease camera elevation angle ‘Down’: Increase camera elevation angle ‘Left’: Decrease camera azimuth angle ‘Right’: Increase camera azimuth angle
- show_view(view=None, roll=None, distance=None, *, row=None, col=None, hemi=None, align=True, azimuth=None, elevation=None, focalpoint=None)[source]#
Orient camera to display view.
- Parameters:
- view
str
|None
The name of the view to show (e.g. “lateral”). Other arguments take precedence and modify the camera starting from the
view
. SeeBrain.show_view
for valid string shortcut options.- roll
float
|None
The roll of the camera rendering the view in degrees.
- distance
float
|None
The distance from the camera rendering the view to the focalpoint in plot units (either m or mm).
- row
int
|None
The row to set. Default all rows.
- col
int
|None
The column to set. Default all columns.
- hemi
str
|None
Which hemi to use for view lookup (when in “both” mode).
- align
bool
If True, consider view arguments relative to canonical MRI directions (closest to MNI for the subject) rather than native MRI space. This helps when MRIs are not in standard orientation (e.g., have large rotations).
- azimuth
float
The azimuthal angle of the camera rendering the view in degrees.
- elevation
float
The The zenith angle of the camera rendering the view in degrees.
- focalpoint
tuple
, shape (3,) |None
The focal point of the camera rendering the view: (x, y, z) in plot units (either m or mm).
- view
Notes
The builtin string views are the following perspectives, based on the RAS convention. If not otherwise noted, the view will have the top of the brain (superior, +Z) in 3D space shown upward in the 2D perspective:
'lateral'
From the left or right side such that the lateral (outside) surface of the given hemisphere is visible.
'medial'
From the left or right side such that the medial (inside) surface of the given hemisphere is visible (at least when in split or single-hemi mode).
'rostral'
From the front.
'caudal'
From the rear.
'dorsal'
From above, with the front of the brain pointing up.
'ventral'
From below, with the front of the brain pointing up.
'frontal'
From the front and slightly lateral, with the brain slightly tilted forward (yielding a view from slightly above).
'parietal'
From the rear and slightly lateral, with the brain slightly tilted backward (yielding a view from slightly above).
'axial'
From above with the brain pointing up (same as
'dorsal'
).'sagittal'
From the right side.
'coronal'
From the rear.
Three letter abbreviations (e.g.,
'lat'
) of all of the above are also supported.Examples using
show_view
:Importing data from fNIRS devices
Importing data from fNIRS devicesPreprocessing functional near-infrared spectroscopy (fNIRS) data
Preprocessing functional near-infrared spectroscopy (fNIRS) dataHow MNE uses FreeSurfer’s outputs
How MNE uses FreeSurfer's outputsVisualize source time courses (stcs)
Visualize source time courses (stcs)Repeated measures ANOVA on source data with spatio-temporal clustering
Repeated measures ANOVA on source data with spatio-temporal clusteringLocating intracranial electrode contacts
Locating intracranial electrode contactsWorking with sEEG dataDICS for power mappingPlotting with ``mne.viz.Brain``Generate a functional label from source estimates
Generate a functional label from source estimatesPlot point-spread functions (PSFs) and cross-talk functions (CTFs)
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
- property time_interpolation#
The interpolation mode.
Examples using mne.viz.Brain
#
Importing data from fNIRS devices
Working with CTF data: the Brainstorm auditory dataset
Preprocessing functional near-infrared spectroscopy (fNIRS) data
How MNE uses FreeSurfer’s outputs
The SourceEstimate data structure
Source localization with MNE, dSPM, sLORETA, and eLORETA
The role of dipole orientations in distributed source localization
Computing various MNE solutions
Source reconstruction using an LCMV beamformer
Visualize source time courses (stcs)
EEG source localization given electrode locations on an MRI
Permutation t-test on source data with spatio-temporal clustering
2 samples permutation test on source data with spatio-temporal clustering
Repeated measures ANOVA on source data with spatio-temporal clustering
Locating intracranial electrode contacts
Corrupt known signal with point spread
Simulate raw data using subject anatomy
Make figures more publication ready
Compute Power Spectral Density of inverse solution from single epochs
Compute source power spectral density (PSD) of VectorView and OPM data
Display sensitivity maps for EEG and MEG sensors
Compute source power using DICS beamformer
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Generate a functional label from source estimates
Compute MNE inverse solution on evoked data with a mixed source space
Compute source power estimate by projecting the covariance with MNE
Visualize source leakage among labels using a circular graph
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers
Plot point-spread functions (PSFs) for a volume
Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG
Compute MxNE with time-frequency sparse prior
Plotting the full vector-valued MNE solution
Optically pumped magnetometer (OPM) data