- mne.viz.plot_epochs(epochs, picks=None, scalings=None, n_epochs=20, n_channels=20, title=None, events=None, event_color=None, order=None, show=True, block=False, decim='auto', noise_cov=None, butterfly=False, show_scrollbars=True, show_scalebars=True, epoch_colors=None, event_id=None, group_by='type', precompute=None, use_opengl=None, *, theme=None, overview_mode=None)#
Bad epochs can be marked with a left click on top of the epoch. Bad channels can be selected by clicking the channel name on the left side of the main axes. Calling this function drops all the selected bad epochs as well as bad epochs marked beforehand with rejection parameters.
- epochsinstance of
The epochs object.
str| array_like |
Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g.,
['meg', 'eeg']) will pick channels of those types, channel name strings (e.g.,
['MEG0111', 'MEG2623']will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good data channels. Note that channels in
info['bads']will be included if their names or indices are explicitly provided.
- scalings‘auto’ |
Scaling factors for the traces. If a dictionary where any value is
'auto', the scaling factor is set to match the 99.5th percentile of the respective data. If
'auto', all scalings (for all channel types) are set to
'auto'. If any values are
'auto'and the data is not preloaded, a subset up to 100 MB will be loaded. If
None, defaults to:
dict(mag=1e-12, grad=4e-11, eeg=20e-6, eog=150e-6, ecg=5e-4, emg=1e-3, ref_meg=1e-12, misc=1e-3, stim=1, resp=1, chpi=1e-4, whitened=1e2)
A particular scaling value
scorresponds to half of the visualized signal range around zero (i.e. from
-s). For example, the default scaling of
20e-6(20µV) for EEG signals means that the visualized range will be 40 µV (20 µV in the positive direction and 20 µV in the negative direction).
The number of epochs per view. Defaults to 20.
The number of channels per view. Defaults to 20.
The title of the window. If None, epochs name will be displayed. Defaults to None.
array, shape (n_events, 3)
Events to show with vertical bars. You can use
plot_eventsas a legend for the colors. By default, the coloring scheme is the same. Defaults to
If the epochs have been resampled, the events no longer align with the data.
New in version 0.14.0.
- event_colorcolor object |
Color(s) to use for events. To show all events in the same color, pass any matplotlib-compatible color. To color events differently, pass a
dictthat maps event names or integer event numbers to colors (must include entries for all events, or include a “fallback” entry with key
None, colors are chosen from the current Matplotlib color cycle. Defaults to
Order in which to plot channel types.
New in version 0.18.0.
Show figure if True. Defaults to True.
Whether to halt program execution until the figure is closed. Useful for rejecting bad trials on the fly by clicking on an epoch. Defaults to False.
Amount to decimate the data during display for speed purposes. You should only decimate if the data are sufficiently low-passed, otherwise aliasing can occur. The ‘auto’ mode (default) uses the decimation that results in a sampling rate at least three times larger than
info['lowpass'](e.g., a 40 Hz lowpass will result in at least a 120 Hz displayed sample rate).
New in version 0.15.0.
- noise_covinstance of
Noise covariance used to whiten the data while plotting. Whitened data channels are scaled by
scalings['whitened'], and their channel names are shown in italic. Can be a string to load a covariance from disk. See also
mne.Evoked.plot_white()for additional inspection of noise covariance properties when whitening evoked data. For data processed with SSS, the effective dependence between magnetometers and gradiometers may introduce differences in scaling, consider using
New in version 0.16.0.
Whether to directly call the butterfly view.
New in version 0.18.0.
Whether to show scrollbars when the plot is initialized. Can be toggled after initialization by pressing z (“zen mode”) while the plot window is focused. Default is
New in version 0.19.0.
Whether to show scale bars when the plot is initialized. Can be toggled after initialization by pressing s while the plot window is focused. Default is
New in version 0.24.0.
list(of n_channels) |
Colors to use for individual epochs. If None, use default colors.
Dictionary of event labels (e.g. ‘aud_l’) as keys and associated event integers as values. Useful when
eventscontains event numbers not present in
epochs.event_id(e.g., because of event subselection). Values in
event_idwill take precedence over those in
epochs.event_idwhen there are overlapping keys.
New in version 0.20.
How to group channels.
'type'groups by channel type,
'original'plots in the order of ch_names,
'selection'uses Elekta’s channel groupings (only works for Neuromag data),
'position'groups the channels by the positions of the sensors.
'position'modes allow custom selections by using a lasso selector on the topomap. In butterfly mode,
'original'group the channels by type, whereas
'position'use regional grouping.
'original'modes are ignored when
None. Defaults to
Whether to load all data (not just the visible portion) into RAM and apply preprocessing (e.g., projectors) to the full data array in a separate processor thread, instead of window-by-window during scrolling. The default None uses the
MNE_BROWSER_PRECOMPUTEvariable, which defaults to
'auto'compares available RAM space to the expected size of the precomputed data, and precomputes only if enough RAM is available. This is only used with the Qt backend.
New in version 0.24.
Changed in version 1.0: Support for the MNE_BROWSER_PRECOMPUTE config variable.
Whether to use OpenGL when rendering the plot (requires
pyopengl). May increase performance, but effect is dependent on system CPU and graphics hardware. Only works if using the Qt backend. Default is None, which will use False unless the user configuration variable
MNE_BROWSER_USE_OPENGLis set to
New in version 0.24.
Can be “auto”, “light”, or “dark” or a path-like to a custom stylesheet. For Dark-Mode and automatic Dark-Mode-Detection,
qdarkstyleand darkdetect, respectively, are required. If None (default), the config option MNE_BROWSER_THEME will be used, defaulting to “auto” if it’s not found. Only supported by the
New in version 1.0.
Can be “channels”, “empty”, or “hidden” to set the overview bar mode for the
'qt'backend. If None (default), the config option
MNE_BROWSER_OVERVIEW_MODEwill be used, defaulting to “channels” if it’s not found.
New in version 1.1.
- epochsinstance of
The arrow keys (up/down/left/right) can be used to navigate between channels and epochs and the scaling can be adjusted with - and + (or =) keys, but this depends on the backend matplotlib is configured to use (e.g., mpl.use(
TkAgg) should work). Full screen mode can be toggled with f11 key. The amount of epochs and channels per view can be adjusted with home/end and page down/page up keys.
hkey plots a histogram of peak-to-peak values along with the used rejection thresholds. Butterfly plot can be toggled with
bkey. Left mouse click adds a vertical line to the plot. Click ‘help’ button at bottom left corner of the plotter to view all the options.
MNE-Python provides two different backends for browsing plots (i.e.,
ica.plot_sources()). One is based on
matplotlib, and the other is based on PyQtGraph. You can set the backend temporarily with the context manager
mne.viz.use_browser_backend(), you can set it for the duration of a Python session using
mne.viz.set_browser_backend(), and you can set the default for your computer via
For the PyQtGraph backend to run in IPython with
block=Falseyou must run the magic command
To report issues with the PyQtGraph backend, please use the issues of
New in version 0.10.0.