mne.viz.plot_raw(raw, events=None, duration=10.0, start=0.0, n_channels=20, bgcolor='w', color=None, bad_color=(0.8, 0.8, 0.8), event_color='cyan', scalings=None, remove_dc=True, order='type', show_options=False, title=None, show=True, block=False, highpass=None, lowpass=None, filtorder=4, clipping=None)

Plot raw data


raw : instance of Raw

The raw data to plot.

events : array | None

Events to show with vertical bars.

duration : float

Time window (sec) to plot. The lesser of this value and the duration of the raw file will be used.

start : float

Initial time to show (can be changed dynamically once plotted).

n_channels : int

Number of channels to plot at once. Defaults to 20. Has no effect if order is ‘position’ or ‘selection’.

bgcolor : color object

Color of the background.

color : dict | color object | None

Color for the data traces. If None, defaults to:

dict(mag='darkblue', grad='b', eeg='k', eog='k', ecg='m',
     emg='k', ref_meg='steelblue', misc='k', stim='k',
     resp='k', chpi='k')

bad_color : color object

Color to make bad channels.

event_color : color object | dict

Color to use for events. Can also be a dict with {event_number: color} pairings. Use event_number==-1 for any event numbers in the events list that are not in the dictionary.

scalings : dict | 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 times up to 100mb 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)

remove_dc : bool

If True remove DC component when plotting data.

order : str | array of int

Order in which to plot data. ‘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 lasso selector on the topomap. Pressing ctrl key while selecting allows appending to the current selection. If array, only the channels in the array are plotted in the given order. Defaults to ‘type’.

show_options : bool

If True, a dialog for options related to projection is shown.

title : str | None

The title of the window. If None, and either the filename of the raw object or ‘<unknown>’ will be displayed as title.

show : bool

Show figure if True.

block : bool

Whether to halt program execution until the figure is closed. Useful for setting bad channels on the fly by clicking on a line. May not work on all systems / platforms.

highpass : float | None

Highpass to apply when displaying data.

lowpass : float | None

Lowpass to apply when displaying data.

filtorder : int

Filtering order. Note that for efficiency and simplicity, filtering during plotting uses forward-backward IIR filtering, so the effective filter order will be twice filtorder. Filtering the lines for display may also produce some edge artifacts (at the left and right edges) of the signals during display. Filtering requires scipy >= 0.10.

clipping : str | None

If None, channels are allowed to exceed their designated bounds in the plot. If “clamp”, then values are clamped to the appropriate range for display, creating step-like artifacts. If “transparent”, then excessive values are not shown, creating gaps in the traces.


fig : Instance of matplotlib.figure.Figure

Raw traces.


The arrow keys (up/down/left/right) can typically be used to navigate between channels and time ranges, but this depends on the backend matplotlib is configured to use (e.g., mpl.use(‘TkAgg’) should work). The scaling can be adjusted with - and + (or =) keys. The viewport dimensions can be adjusted with page up/page down and home/end keys. Full screen mode can be to toggled with f11 key. To mark or un-mark a channel as bad, click on the rather flat segments of a channel’s time series. The changes will be reflected immediately in the raw object’s['bads'] entry.