Visualization routines.

Brain(subject[, hemi, surf, title, cortex, ...])

Class for visualizing a brain.

ClickableImage(imdata, **kwargs)

Display an image so you can click on it and store x/y positions.

EvokedField(evoked, surf_maps, *[, time, ...])

Plot MEG/EEG fields on head surface and helmet in 3D.


Class that refers to a 3D figure.

add_background_image(fig, im[, set_ratios])

Add a background image to a plot.


Convert center points to edges.

compare_fiff(fname_1, fname_2[, fname_out, ...])

Compare the contents of two fiff files using diff and show_fiff.

circular_layout(node_names, node_order[, ...])

Create layout arranging nodes on a circle.

iter_topography(info[, layout, on_pick, ...])

Create iterator over channel positions.

mne_analyze_colormap([limits, format])

Return a colormap similar to that used by mne_analyze.

plot_bem(subject[, subjects_dir, ...])

Plot BEM contours on anatomical MRI slices.

plot_brain_colorbar(ax, clim[, colormap, ...])

Plot a colorbar that corresponds to a brain activation map.

plot_bridged_electrodes(info, bridged_idx, ...)

Topoplot electrode distance matrix with bridged electrodes connected.

plot_chpi_snr(snr_dict[, axes])

Plot time-varying SNR estimates of the HPI coils.

plot_cov(cov, info[, exclude, colorbar, ...])

Plot Covariance data.

plot_channel_labels_circle(labels[, colors, ...])

Plot labels for each channel in a circle plot.

plot_ch_adjacency(info, adjacency, ch_names)

Plot channel adjacency.

plot_csd(csd[, info, mode, colorbar, cmap, ...])

Plot CSD matrices.

plot_dipole_amplitudes(dipoles[, colors, show])

Plot the amplitude traces of a set of dipoles.

plot_dipole_locations(dipoles[, trans, ...])

Plot dipole locations.

plot_drop_log(drop_log[, threshold, ...])

Show the channel stats based on a drop_log from Epochs.

plot_epochs(epochs[, picks, scalings, ...])

Visualize epochs.

plot_epochs_psd_topomap(epochs[, bands, ...])


LEGACY: New code should use Epochs.compute_psd().plot_topomap().

plot_events(events[, sfreq, first_samp, ...])

Plot events to get a visual display of the paradigm.

plot_evoked(evoked[, picks, exclude, unit, ...])

Plot evoked data using butterfly plots.

plot_evoked_image(evoked[, picks, exclude, ...])

Plot evoked data as images.

plot_evoked_topo(evoked[, layout, ...])

Plot 2D topography of evoked responses.

plot_evoked_topomap(evoked[, times, ...])

Plot topographic maps of specific time points of evoked data.

plot_evoked_joint(evoked[, times, title, ...])

Plot evoked data as butterfly plot and add topomaps for time points.

plot_evoked_field(evoked, surf_maps[, time, ...])

Plot MEG/EEG fields on head surface and helmet in 3D.

plot_evoked_white(evoked, noise_cov[, show, ...])

Plot whitened evoked response.

plot_filter(h, sfreq[, freq, gain, title, ...])

Plot properties of a filter.

plot_head_positions(pos[, mode, cmap, ...])

Plot head positions.

plot_ideal_filter(freq, gain[, axes, title, ...])

Plot an ideal filter response.

plot_compare_evokeds(evokeds[, picks, ...])

Plot evoked time courses for one or more conditions and/or channels.

plot_ica_sources(ica, inst[, picks, start, ...])

Plot estimated latent sources given the unmixing matrix.

plot_ica_components(ica[, picks, ch_type, ...])

Project mixing matrix on interpolated sensor topography.

plot_ica_properties(ica, inst[, picks, ...])

Display component properties.

plot_ica_scores(ica, scores[, exclude, ...])

Plot scores related to detected components.

plot_ica_overlay(ica, inst[, exclude, ...])

Overlay of raw and cleaned signals given the unmixing matrix.

plot_epochs_image(epochs[, picks, sigma, ...])

Plot Event Related Potential / Fields image.

plot_layout(layout[, picks, show_axes, show])

Plot the sensor positions.

plot_montage(montage[, scale_factor, ...])

Plot a montage.

plot_projs_topomap(projs, info, *[, ...])

Plot topographic maps of SSP projections.

plot_projs_joint(projs, evoked[, ...])

Plot projectors and evoked jointly.

plot_raw(raw[, events, duration, start, ...])

Plot raw data.

plot_raw_psd(raw[, fmin, fmax, tmin, tmax, ...])


LEGACY: New code should use Raw.compute_psd().plot().

plot_regression_weights(model, *[, ch_type, ...])

Plot the regression weights of a fitted EOGRegression model.

plot_sensors(info[, kind, ch_type, title, ...])

Plot sensors positions.

plot_snr_estimate(evoked, inv[, show, axes, ...])

Plot a data SNR estimate.

plot_source_estimates(stc[, subject, ...])

Plot SourceEstimate.

link_brains(brains[, time, camera, ...])

Plot multiple SourceEstimate objects with PyVista.

plot_volume_source_estimates(stc, src[, ...])

Plot Nutmeg style volumetric source estimates using nilearn.

plot_vector_source_estimates(stc[, subject, ...])

Plot VectorSourceEstimate with PyVista.

plot_sparse_source_estimates(src, stcs[, ...])

Plot source estimates obtained with sparse solver.

plot_tfr_topomap(tfr[, tmin, tmax, fmin, ...])

Plot topographic maps of specific time-frequency intervals of TFR data.

plot_topo_image_epochs(epochs[, layout, ...])

Plot Event Related Potential / Fields image on topographies.

plot_topomap(data, pos, *[, ch_type, ...])

Plot a topographic map as image.

plot_alignment([info, trans, subject, ...])

Plot head, sensor, and source space alignment in 3D.

snapshot_brain_montage(fig, montage[, ...])

Take a snapshot of a PyVista Scene and project channels onto 2d coords.

plot_arrowmap(data, info_from[, info_to, ...])

Plot arrow map.

set_3d_backend(backend_name[, verbose])

Set the 3D backend for MNE.


Return the 3D backend currently used.


Create a 3d visualization context using the designated backend.

set_3d_options([antialias, depth_peeling, ...])

Set 3D rendering options.

set_3d_view(figure[, azimuth, elevation, ...])

Configure the view of the given scene.

set_3d_title(figure, title[, size])

Configure the title of the given scene.

create_3d_figure(size[, bgcolor, ...])

Return an empty figure based on the current 3d backend.


Close the given scene.


Close all the scenes of the current 3d backend.


Return the proper Brain class based on the current 3d backend.

set_browser_backend(backend_name[, verbose])

Set the 2D browser backend for MNE.


Return the 2D backend currently used.


Create a 2D browser visualization context using the designated backend.



Eye-tracking visualization routines.

plot_gaze(epochs, *[, calibration, width, ...])

Plot a heatmap of eyetracking gaze data.

UI Events#


Event API for inter-figure communication.

The event API allows figures to communicate with each other, such that a change in one figure can trigger a change in another figure. For example, moving the time cursor in one plot can update the current time in another plot. Another scenario is two drawing routines drawing into the same window, using events to stay in-sync.

Authors: Marijn van Vliet <>

subscribe(fig, event_name, callback, *[, ...])

Subscribe to an event on a figure's event channel.

unsubscribe(fig, event_names[, callback, ...])

Unsubscribe from an event on a figure's event channel.

publish(fig, event, *[, verbose])

Publish an event to all subscribers of the figure's channel.

link(*figs[, include_events, ...])

Link the event channels of two figures together.

unlink(fig, *[, verbose])

Remove all links involving the event channel of the given figure.


Temporarily disable generation of UI events.


Abstract base class for all events.

ColormapRange(kind[, ch_type, fmin, fmid, ...])

Indicates that the user has updated the bounds of the colormap.

Contours(kind, contours)

Indicates that the user has changed the contour lines.


Indicates that the user has requested to close a figure.


Indicates that the user has selected a different playback speed for videos.


Indicates that the user has selected a time.

VertexSelect(hemi, vertex_id)

Indicates that the user has selected a vertex.