Plot a topographic map as image.
array
, shape (n_chan,)The data values to plot.
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 the montage applied to the Info
object.
float
| callable()
| None
Lower and upper bounds of the colormap, in the same units as the data.
If vmin
and 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 vmin
, vmax
is None
, will use
min(data)
or max(data)
, respectively. If callable, should
accept a NumPy array
of data and return a
float.
None
Colormap to use. If None, ‘Reds’ is used for all positive data, otherwise defaults to ‘RdBu_r’.
str
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.
int
The resolution of the topomap image (number of pixels along each side).
Axes
| None
The axes to plot to. If None, the current axes will be used.
list
| None
List of channel names. If None, channel names are not plotted.
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).
ndarray
of bool, shape (n_channels,) | None
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 None
,
equivalent to an array of all False
elements.
dict
| None
Additional plotting parameters for plotting significant sensors. Default (None) equals:
dict(marker='o', markerfacecolor='w', markeredgecolor='k',
linewidth=0, markersize=4)
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. 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’.
int
| array
of float
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 6
.
str
The image interpolation to be used. Options are 'cubic'
(default)
to use scipy.interpolate.CloughTocher2DInterpolator
,
'nearest'
to use scipy.spatial.Voronoi
or
'linear'
to use scipy.interpolate.LinearNDInterpolator
.
Show figure if True.
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.
str
Options:
'box'
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 ConductorModel
| None
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.
float
| ‘mean’Value to extrapolate to on the topomap borders. If 'mean'
(default),
then each extrapolated point has the average value of its neighbours.
New in version 0.20.
str
The channel type being plotted. Determines the 'auto'
extrapolation mode.
New in version 0.21.
matplotlib.colors.Normalize
| None
Colormap normalization, default None means linear normalization. If not
None, vmin
and vmax
arguments are ignored. See Notes for more
details.
New in version 0.24.
matplotlib.image.AxesImage
The interpolated data.
matplotlib.contour.ContourSet
The fieldlines.
Notes
The 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
normalization cnorm
and pass it as the the cnorm
argument:
from matplotlib.colors import TwoSlopeNorm
cnorm = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=3)
Note that because we define vmin
and vmax
in the normalization,
arguments vmin
and vmax
to plot_topomap
will be ignored if a
normalization is provided. See the
matplotlib docs
for more details on colormap normalization.
mne.viz.plot_topomap
#Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset
Identify EEG Electrodes Bridged by too much Gel
Plotting topographic maps of evoked data
Receptive Field Estimation and Prediction