Plot a topographic map as image.
array, shape (n_chan,)
The data values to plot.
array, shape (n_chan, 2) | instance of
Location information for the data points(/channels).
If an array, for each data point, the x and y coordinates.
Info object, it must contain only one data type and
len(data) data channels, and the x/y coordinates will
be inferred from the montage applied to the
Lower and upper bounds of the colormap, in the same units as the data.
vmax are both
None, they are set at ± the
maximum absolute value of the data (yielding a colormap with midpoint
at 0). If only one of
None, will use
max(data), respectively. If callable, should
NumPy array of data and return a
Colormap to use. If None, ‘Reds’ is used for all positive data, otherwise defaults to ‘RdBu_r’.
Add markers for sensor locations to the plot. Accepts matplotlib plot
format string (e.g.,
'r+' for red plusses). If True (default),
circles will be used.
The resolution of the topomap image (number of pixels along each side).
The axes to plot to. If None, the current axes will be used.
List of channel names. If None, channel names are not plotted.
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.
True, a list of names must be provided (see
bool, shape (n_channels,) |
Array indicating channel(s) to highlight with a distinct
plotting style. Array elements set to
True will be plotted
with the parameters given in
mask_params. Defaults to
equivalent to an array of all
Additional plotting parameters for plotting significant sensors. Default (None) equals:
dict(marker='o', markerfacecolor='w', markeredgecolor='k', linewidth=0, markersize=4)
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. 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’.
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 µV for EEG, fT for magnetometers and fT/m for
gradiometers. Defaults to
The image interpolation to be used. Options are
'nearest' to use
'linear' to use
Show figure if True.
Handle for a function that is called when the user selects a set of
channels by rectangle selection (matplotlib
None interactive selection is disabled. Defaults to None.
Extrapolate to four points placed to form a square encompassing all data points, where each side of the square is three times the range of the data in the respective dimension.
'local'(default for MEG sensors)
Extrapolate only to nearby points (approximately to points closer than median inter-electrode distance). This will also set the mask to be polygonal based on the convex hull of the sensors.
'head'(default for non-MEG sensors)
Extrapolate out to the edges of the clipping circle. This will be on the head circle when the sensors are contained within the head circle, but it can extend beyond the head when sensors are plotted outside the head circle.
Changed in version 0.21:
The default was changed to
'local' for MEG sensors.
'local' was changed to use a convex hull mask
'head' was changed to extrapolate out to the clipping circle.
New in version 0.18.
float| array-like | instance of
The sphere parameters to use for the head outline. Can be array-like of
shape (4,) to give the X/Y/Z origin and radius in meters, or a single float
to give just the radius (origin assumed 0, 0, 0). Can also be an instance
of a spherical
ConductorModel to use the origin and
radius from that object.
None (the default) is equivalent to (0, 0, 0,
0.095). Currently the head radius does not affect plotting.
New in version 0.20.
Value to extrapolate to on the topomap borders. If
then each extrapolated point has the average value of its neighbours.
New in version 0.20.
The channel type to plot. For
'grad', the gradiometers are
collected in pairs and the RMS for each pair is plotted. If
None the first available channel type from order shown above is
used. Defaults to
New in version 0.21.
Colormap normalization, default None means linear normalization. If not
vmax arguments are ignored. See Notes for more
New in version 0.24.
cnorm parameter can be used to implement custom colormap
normalization. By default, a linear mapping from vmin to vmax is used,
which correspond to the first and last colors in the colormap. This might
be undesired when vmin and vmax are not symmetrical around zero (or a value
that can be interpreted as some midpoint). For example, assume we want to
use the RdBu colormap (red to white to blue) for values ranging from -1 to
3, and 0 should be white. However, white corresponds to the midpoint in the
data by default, i.e. 1. Therefore, we use the following colormap
cnorm and pass it as the the
from matplotlib.colors import TwoSlopeNorm cnorm = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=3)
Note that because we define
vmax in the normalization,
plot_topomap will be ignored if a
normalization is provided. See the
for more details on colormap normalization.