epochs : instance of Epochs
picks : int | array-like of int | None
The indices of the channels to consider. If None, all good
data channels are plotted.
sigma : float
The standard deviation of the Gaussian smoothing to apply along
the epoch axis to apply in the image. If 0., no smoothing is applied.
vmin : float
The min value in the image. The unit is uV for EEG channels,
fT for magnetometers and fT/cm for gradiometers
vmax : float
The max value in the image. The unit is uV for EEG channels,
fT for magnetometers and fT/cm for gradiometers
colorbar : bool
Display or not a colorbar
order : None | array of int | callable
If not None, order is used to reorder the epochs on the y-axis
of the image. If it’s an array of int it should be of length
the number of good epochs. If it’s a callable the arguments
passed are the times vector and the data as 2d array
(data.shape[1] == len(times)
show : bool
units : dict | None
The units of the channel types used for axes lables. If None,
defaults to units=dict(eeg=’uV’, grad=’fT/cm’, mag=’fT’).
scalings : dict | None
The scalings of the channel types to be applied for plotting.
If None, defaults to scalings=dict(eeg=1e6, grad=1e13, mag=1e15,
eog=1e6)
cmap : matplotlib colormap
fig : matplotlib figure | None
Figure instance to draw the image to. Figure must contain two axes for
drawing the single trials and evoked responses. If None a new figure is
created. Defaults to None.
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