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:
- events
array
ofint
, 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.
- sfreq
float
|None
The sample frequency. If None, data will be displayed in samples (not seconds).
- first_samp
int
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.- color
dict
|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_id
dict
|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_spacing
bool
Use equal spacing between events in y-axis.
- show
bool
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 fromevent_id
are missing from events. When numbers from events are missing fromevent_id
they will be ignored and a warning emitted; consider usingverbose='error'
in this case.New in version 0.21.
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- events
- Returns:
- fig
matplotlib.figure.Figure
The figure object containing the plot.
- fig
Notes
New in version 0.9.0.
Examples using mne.viz.plot_events
#
Overview of MEG/EEG analysis with MNE-Python
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Single trial linear regression analysis with the LIMO dataset
From raw data to dSPM on SPM Faces dataset