mne.viz.plot_events#

mne.viz.plot_events(events, sfreq=None, first_samp=0, color=None, event_id=None, axes=None, equal_spacing=True, show=True, on_missing='raise', verbose=None)[source]#

Plot events to get a visual display of the paradigm.

Parameters:
eventsndarray of int, shape (n_events, 3)

The identity and timing of experimental events, around which the epochs were created. See events for more information.

sfreqfloat | None

The sample frequency. If None, data will be displayed in samples (not seconds).

first_sampint

The index of the first sample. Recordings made on Neuromag systems number samples relative to the system start (not relative to the beginning of the recording). In such cases the raw.first_samp attribute can be passed here. Default is 0.

colordict | None

Dictionary of event_id integers as keys and colors as values. If None, colors are automatically drawn from a default list (cycled through if number of events longer than list of default colors). Color can be any valid matplotlib color.

event_iddict | None

Dictionary of event labels (e.g. ‘aud_l’) as keys and their associated event_id values. Labels are used to plot a legend. If None, no legend is drawn.

axesinstance of Axes

The subplot handle.

equal_spacingbool

Use equal spacing between events in y-axis.

showbool

Show figure if True.

on_missing‘raise’ | ‘warn’ | ‘ignore’

Can be 'raise' (default) to raise an error, 'warn' to emit a warning, or 'ignore' to ignore when event numbers from event_id are missing from events. When numbers from events are missing from event_id they will be ignored and a warning emitted; consider using verbose='error' in this case.

New in v0.21.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
figmatplotlib.figure.Figure

The figure object containing the plot.

Notes

New in v0.9.0.

Examples using mne.viz.plot_events#

Overview of MEG/EEG analysis with MNE-Python

Overview of MEG/EEG analysis with MNE-Python

Working with events

Working with events

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Sleep stage classification from polysomnography (PSG) data

Sleep stage classification from polysomnography (PSG) data

Automated epochs metadata generation with variable time windows

Automated epochs metadata generation with variable time windows

Single trial linear regression analysis with the LIMO dataset

Single trial linear regression analysis with the LIMO dataset