Parameters: |
- epochs : instance of Epochs
The epochs.
- picks : int | array-like of int | None
The indices of the channels to consider. If None and combine is
also None, the first five good 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.
Defaults to 0.
- vmin : None | float | callable
The min value in the image (and the ER[P/F]). The unit is uV for
EEG channels, fT for magnetometers and fT/cm for gradiometers.
If vmin is None and multiple plots are returned, the limit is
equalized within channel types.
Hint: to specify the lower limit of the data, use
vmin=lambda data: data.min() .
- vmax : None | float | callable
The max value in the image (and the ER[P/F]). The unit is uV for
EEG channels, fT for magnetometers and fT/cm for gradiometers.
If vmin is None and multiple plots are returned, the limit is
equalized within channel types.
- 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
Show figure if True.
- 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 : None | matplotlib colormap | (colormap, bool) | ‘interactive’
Colormap. If tuple, the first value indicates the colormap to use and
the second value is a boolean defining interactivity. In interactive
mode the colors are adjustable by clicking and dragging the colorbar
with left and right mouse button. Left mouse button moves the scale up
and down and right mouse button adjusts the range. Hitting space bar
resets the scale. Up and down arrows can be used to change the
colormap. If ‘interactive’, translates to (‘RdBu_r’, True).
If None, “RdBu_r” is used, unless the data is all positive, in which
case “Reds” is used.
- 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.
- axes : list of matplotlib axes | dict of lists of matplotlib Axes | None
List of axes instances to draw the image, erp and colorbar to.
Must be of length three if colorbar is True (with the last list element
being the colorbar axes) or two if colorbar is False. If both fig and
axes are passed, an error is raised.
If group_by is a dict, this cannot be a list, but it can be a dict
of lists of axes, with the keys matching those of group_by . In that
case, the provided axes will be used for the corresponding groups.
Defaults to None.
- overlay_times : array-like, shape (n_epochs,) | None
If not None the parameter is interpreted as time instants in seconds
and is added to the image. It is typically useful to display reaction
times. Note that it is defined with respect to the order
of epochs such that overlay_times[0] corresponds to epochs[0].
- combine : None | str | callable
If None, return one figure per pick. If not None, aggregate over
channels via the indicated method. If str, must be one of “mean”,
“median”, “std” or “gfp”, in which case the mean, the median, the
standard deviation or the GFP over channels are plotted.
array (n_epochs, n_times).
If callable, it must accept one positional input, the data
in the format (n_epochs, n_channels, n_times). It must return an
array (n_epochs, n_times). For example:
combine = lambda data: np.median(data, axis=1)
Defaults to None if picks are provided, otherwise ‘gfp’.
- group_by : None | str | dict
If not None, combining happens over channel groups defined by this
parameter.
If str, must be “type”, in which case one figure per channel type is
returned (combining within channel types).
If a dict, the values must be picks and one figure will be returned
for each entry, aggregating over the corresponding pick groups; keys
will become plot titles. This is useful for e.g. ROIs. Each entry must
contain only one channel type. For example:
group_by=dict(Left_ROI=[1, 2, 3, 4], Right_ROI=[5, 6, 7, 8])
If not None, combine must not be None. Defaults to None if picks are
provided, otherwise ‘type’.
- evoked : Bool
Draw the ER[P/F] below the image or not.
- ts_args : dict
Arguments passed to a call to mne.viz.plot_compare_evoked to style
the evoked plot below the image. Defaults to an empty dictionary,
meaning plot_compare_evokeds will be called with default parameters
(yaxis truncation will be turned off).
- title : None | str
If str, will be plotted as figure title. Else, the channels will be
indicated.
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