Visualization

mne.viz:

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.
Figure3D()	Class that refers to a 3D figure.
add_background_image(fig, im[, set_ratios])	Add a background image to a plot.
centers_to_edges(*arrays)	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, ...])	Warning 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, ...])	Warning 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.
get_3d_backend()	Return the 3D backend currently used.
use_3d_backend(backend_name)	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_3d_figure(figure)	Close the given scene.
close_all_3d_figures()	Close all the scenes of the current 3d backend.
get_brain_class()	Return the proper Brain class based on the current 3d backend.
set_browser_backend(backend_name[, verbose])	Set the 2D browser backend for MNE.
get_browser_backend()	Return the 2D backend currently used.
use_browser_backend(backend_name)	Create a 2D browser visualization context using the designated backend.