| Parameters: | evoked : Evoked
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, at most 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, bool) | ‘interactive’ | None
Colormap to use. 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 (zoom).
The mouse scroll can also be used to adjust the range. Hitting space
bar resets the range. Up and down arrows can be used to change the
colormap. If None (default), ‘Reds’ is used for all positive data,
otherwise defaults to ‘RdBu_r’. If ‘interactive’, translates to
(None, True).
Warning
Interactive mode works smoothly only for a small amount
of topomaps. Interactive mode is disabled by default for more than
2 topomaps.
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
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_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.
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-axis plots). If None, nothing will be drawn.
Defaults to ‘head’.
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. If colorbar=True, the ticks in colorbar correspond to the
contour levels.
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.
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