| Parameters: | data : array, shape (n_chan,)
pos : array, shape (n_chan, 2) | instance of Info
Location information for the data points(/channels).
If an array, for each data point, the x and y coordinates.
If an Info object, it must contain only one data type and
exactly len(data) data channels, and the x/y coordinates will
be inferred from this Info object.
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 | None
Colormap to use. If None, ‘Reds’ is used for all positive data,
otherwise defaults to ‘RdBu_r’.
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.
res : int
The resolution of the topomap image (n pixels along each side).
axes : instance of Axes | None
The axes to plot to. If None, the current axes will be used.
names : list | None
List of channel names. If None, channel names are not plotted.
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.
If True, a list of names must be provided (see names keyword).
mask : ndarray of bool, shape (n_channels, n_times) | None
The channels to be marked as significant at a given time point.
Indices 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-axes plots). If None, nothing will be drawn.
Defaults to ‘head’.
image_mask : ndarray of bool, shape (res, res) | None
The image mask to cover the interpolated surface. If None, it will be
computed from the outline.
contours : int | False | array of float | None
The number of contour lines to draw. If 0, no contours will be drawn.
If an array, the values represent the levels for the contours. The
values are in uV for EEG, fT for magnetometers and fT/m for
gradiometers.
image_interp : str
The image interpolation to be used. All matplotlib options are
accepted.
show : bool
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.
onselect : callable | None
Handle for a function that is called when the user selects a set of
channels by rectangle selection (matplotlib RectangleSelector). If
None interactive selection is disabled. Defaults to None.
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