data : 1D or 2D ndarray
The data array can either be 1D (samples over time) or 2D
(trials x samples). In the first case a boxcar mapper is used to
extract the respective trial timecourses given a list of trial onsets.
In the latter case, each row of the data array is taken as the EEG
signal timecourse of a particular trial.
onsets : list(int)
List of onsets (in samples not in seconds).
SR : int, optional
Sampling rate (1/s) of the signal.
pre : float, optional
Duration (in seconds) to be plotted prior to onset.
pre_onset : float or None
If data is already in epochs (2D) then pre_onset provides information
on how many seconds pre-stimulus were used to generate them. If None,
then pre_onset = pre
post : float
Duration (in seconds) to be plotted after the onset.
pre_mean : float
Duration (in seconds) at the beginning of the window which is used
for deriving the mean of the signal. If None, pre_mean = pre
errtype : None or ‘ste’ or ‘std’ or ‘ci95’ or list of previous three
Type of error value to be computed per datapoint. ‘ste’ –
standard error of the mean, ‘std’ – standard deviation ‘ci95’
– 95% confidence interval (1.96 * ste), None – no error margin
is plotted (default)
Optionally, multiple error types can be specified in a list. In that
case all of them will be plotted.
color : matplotlib color code, optional
Color to be used for plotting the mean signal timecourse.
errcolor : matplotlib color code
Color to be used for plotting the error margin. If None, use main color
but with weak alpha level
ax : :
ymult : float, optional
Multiplier for the values. E.g. if negative-up ERP plot is needed:
provide ymult=-1.0
*args, **kwargs :
Additional arguments to pylab.plot.
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