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, show_scalebars=True, epoch_colors=None, event_id=None, group_by='type', precompute=None, use_opengl=None, *, theme=None, overview_mode=None)[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
| array_like |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‘auto’ |
dict
|None
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. IfNone
, 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)
Note
A particular scaling value
s
corresponds to half of the visualized signal range around zero (i.e. from0
to+s
or from0
to-s
). For example, the default scaling of20e-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).- 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.
- show
bool
Show figure if True. Defaults to True.
- block
bool
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.
- butterfly
bool
Whether to directly call the butterfly view.
New in version 0.18.0.
- show_scrollbars
bool
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.
- show_scalebars
bool
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
True
.New in version 0.24.0.
- 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'
.- precompute
bool
|str
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_PRECOMPUTE
variable, which defaults to'auto'
.'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.
- use_opengl
bool
|None
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 variableMNE_BROWSER_USE_OPENGL
is set to'true'
, seemne.set_config()
.New in version 0.24.
- theme
str
| path-like Can be “auto”, “light”, or “dark” or a path-like to a custom stylesheet. For Dark-Mode and automatic Dark-Mode-Detection,
qdarkstyle
and 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'qt'
backend.New in version 1.0.
- overview_mode
str
|None
Can be “channels”, “empty”, or “hidden” to set the overview bar mode for the
'qt'
backend. If None (default), the config optionMNE_BROWSER_OVERVIEW_MODE
will be used, defaulting to “channels” if it’s not found.New in version 1.1.
- epochsinstance of
- Returns:
- fig
matplotlib.figure.Figure
| mne_qt_browser.figure.MNEQtBrowser Browser instance.
- fig
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.h
key plots a histogram of peak-to-peak values along with the used rejection thresholds. Butterfly plot can be toggled withb
key. 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.,
raw.plot()
,epochs.plot()
, andica.plot_sources()
). One is based onmatplotlib
, and the other is based on PyQtGraph. You can set the backend temporarily with the context managermne.viz.use_browser_backend()
, you can set it for the duration of a Python session usingmne.viz.set_browser_backend()
, and you can set the default for your computer viamne.set_config('MNE_BROWSER_BACKEND', 'matplotlib')
(or'qt'
).Note
For the PyQtGraph backend to run in IPython with
block=False
you must run the magic command%gui qt5
first.Note
To report issues with the PyQtGraph backend, please use the issues of
mne-qt-browser
.New in version 0.10.0.