mne.viz.plot_epochs¶
- 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, epoch_colors=None, event_id=None, group_by='type')[source]¶
Visualize epochs.
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.
- Parameters
- epochsinstance of
Epochs
The epochs object.
- picks
str
|list
|slice
|None
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 ininfo['bads']
will be included if their names or indices are explicitly provided.- scalings
dict
| ‘auto’ |None
Scaling factors for the traces. If any fields in scalings are ‘auto’, the scaling factor is set to match the 99.5th percentile of a subset of the corresponding data. If scalings == ‘auto’, all scalings fields are set to ‘auto’. If any fields are ‘auto’ and data is not preloaded, a subset of epochs 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=10.)
- n_epochs
int
The number of epochs per view. Defaults to 20.
- n_channels
int
The number of channels per view. Defaults to 20.
- title
str
|None
The title of the window. If None, epochs name will be displayed. Defaults to None.
- events
None
|array
, shape (n_events, 3) Events to show with vertical bars. You can use
plot_events
as a legend for the colors. By default, the coloring scheme is the same. Defaults toNone
.Warning
If the epochs have been resampled, the events no longer align with the data.
New in version 0.14.0.
- event_colorcolor object |
dict
|None
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
dict
that maps event names or integer event numbers to colors (must include entries for all events, or include a “fallback” entry with key-1
). IfNone
, colors are chosen from the current Matplotlib color cycle. Defaults toNone
.- order
array
ofstr
|None
Order in which to plot channel types.
New in version 0.18.0.
- showbool
Show figure if True. Defaults to True.
- blockbool
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.
- decim
int
| ‘auto’ 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
Covariance
|str
|None
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 alsomne.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 usingmne.Evoked.plot_white()
.New in version 0.16.0.
- butterflybool
Whether to directly call the butterfly view.
New in version 0.18.0.
- show_scrollbarsbool
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
True
.New in version 0.19.0.
- epoch_colors
list
of (n_epochs)list
(of n_channels) |None
Colors to use for individual epochs. If None, use default colors.
- event_id
dict
|None
Dictionary of event labels (e.g. ‘aud_l’) as keys and associated event integers as values. Useful when
events
contains event numbers not present inepochs.event_id
(e.g., because of event subselection). Values inevent_id
will take precedence over those inepochs.event_id
when there are overlapping keys.New in version 0.20.
- group_by
str
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.'selection'
and'position'
modes allow custom selections by using a lasso selector on the topomap. In butterfly mode,'type'
and'original'
group the channels by type, whereas'selection'
and'position'
use regional grouping.'type'
and'original'
modes are ignored whenorder
is notNone
. Defaults to'type'
.
- epochsinstance of
- Returns
- figinstance of
matplotlib.figure.Figure
The figure.
- figinstance of
Notes
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. These can also be set through options dialog by pressingo
key.h
key plots a histogram of peak-to-peak values along with the used rejection thresholds. Butterfly plot can be toggled withb
key. Right mouse click adds a vertical line to the plot. Click ‘help’ button at bottom left corner of the plotter to view all the options.New in version 0.10.0.