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
eventsarray of int, shape (n_events, 3)

The array of events. The first column contains the event time in samples, with first_samp included. The third column contains the event id.

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 version 0.21.

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 version 0.9.0.

Examples using mne.viz.plot_events#

Overview of MEG/EEG analysis with MNE-Python

Overview of MEG/EEG analysis with MNE-Python

Overview of MEG/EEG analysis with MNE-Python
Working with events

Working with events

Working with events
Preprocessing functional near-infrared spectroscopy (fNIRS) data

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Single trial linear regression analysis with the LIMO dataset

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From raw data to dSPM on SPM Faces dataset

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From raw data to dSPM on SPM Faces dataset