mne.viz.plot_evoked_topomap(evoked, times='auto', ch_type=None, layout=None, vmin=None, vmax=None, cmap='RdBu_r', sensors=True, colorbar=True, scale=None, scale_time=1000.0, unit=None, res=64, size=1, cbar_fmt='%3.1f', time_format='%01d ms', proj=False, show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', contours=6, image_interp='bilinear', average=None, head_pos=None, axes=None)

Plot topographic maps of specific time points of evoked data


evoked : Evoked

The Evoked object.

times : float | array of floats | “auto” | “peaks”.

The time point(s) to plot. If “auto”, the number of axes determines the amount of time point(s). If axes is also None, 10 topographies will be shown with a regular time spacing between the first and last time instant. If “peaks”, finds time points automatically by checking for local maxima in global field power.

ch_type : ‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ | None

The channel type to plot. For ‘grad’, the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then channels are chosen in the order given above.

layout : None | Layout

Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If possible, the correct layout file is inferred from the data; if no appropriate layout file was found, the layout is automatically generated from the sensor locations.

vmin : float | callable | None

The value specifying the lower bound of the color range. If None, and vmax is None, -vmax is used. Else np.min(data). If callable, the output equals vmin(data). Defaults to None.

vmax : float | callable | None

The value specifying the upper bound of the color range. If None, the maximum absolute value is used. If callable, the output equals vmax(data). Defaults to None.

cmap : matplotlib colormap

Colormap. For magnetometers and eeg defaults to ‘RdBu_r’, else ‘Reds’.

sensors : bool | str

Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., ‘r+’ for red plusses). If True, a circle will be used (via .add_artist). Defaults to True.

colorbar : bool

Plot a colorbar.

scale : dict | float | None

Scale the data for plotting. If None, defaults to 1e6 for eeg, 1e13 for grad and 1e15 for mag.

scale_time : float | None

Scale the time labels. Defaults to 1e3 (ms).

unit : dict | str | None

The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined.

res : int

The resolution of the topomap image (n pixels along each side).

size : float

Side length per topomap in inches.

cbar_fmt : str

String format for colorbar values.

time_format : str

String format for topomap values. Defaults to “%01d ms”

proj : bool | ‘interactive’

If true SSP projections are applied before display. If ‘interactive’, a check box for reversible selection of SSP projection vectors will be show.

show : bool

Show figure if True.

show_names : bool | callable

If True, show channel names on top of the map. If a callable is passed, channel names will be formatted using the callable; e.g., to delete the prefix ‘MEG ‘ from all channel names, pass the function lambda x: x.replace(‘MEG ‘, ‘’). If mask is not None, only significant sensors will be shown.

title : str | None

Title. If None (default), no title is displayed.

mask : ndarray of bool, shape (n_channels, n_times) | None

The channels to be marked as significant at a given time point. Indicies set to True will be considered. Defaults to None.

mask_params : dict | None

Additional plotting parameters for plotting significant sensors. Default (None) equals:

dict(marker='o', markerfacecolor='w', markeredgecolor='k',
     linewidth=0, markersize=4)

outlines : ‘head’ | ‘skirt’ | dict | None

The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If ‘skirt’ the head scheme will be drawn, but sensors are allowed to be plotted outside of the head circle. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask, and the ‘autoshrink’ (bool) field will trigger automated shrinking of the positions due to points outside the outline. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to ‘head’.

contours : int | False | None

The number of contour lines to draw. If 0, no contours will be drawn.

image_interp : str

The image interpolation to be used. All matplotlib options are accepted.

average : float | None

The time window around a given time to be used for averaging (seconds). For example, 0.01 would translate into window that starts 5 ms before and ends 5 ms after a given time point. Defaults to None, which means no averaging.

head_pos : dict | None

If None (default), the sensors are positioned such that they span the head circle. If dict, can have entries ‘center’ (tuple) and ‘scale’ (tuple) for what the center and scale of the head should be relative to the electrode locations.

axes : instance of Axes | list | None

The axes to plot to. If list, the list must be a list of Axes of the same length as times (unless times is None). If instance of Axes, times must be a float or a list of one float. Defaults to None.


fig : instance of matplotlib.figure.Figure

The figure.