- mne.viz.plot_topomap(data, pos, *, ch_type='eeg', sensors=True, names=None, mask=None, mask_params=None, contours=6, outlines='head', sphere=None, image_interp='cubic', extrapolate='auto', border='mean', res=64, size=1, cmap=None, vlim=(None, None), cnorm=None, axes=None, show=True, onselect=None)[source]#
Plot a topographic map as image.
array, shape (n_chan,)
The data values to plot.
array, shape (n_channels, 2) | instance of
Location information for the channels. If an array, should provide the x and y coordinates for plotting the channels in 2D. If an
Infoobject it must contain only one channel type and exactly
len(data)channels; the x/y coordinates will be inferred from the montage in the
- ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ |
The channel type to plot. For
'grad', the gradiometers are collected in pairs and the RMS for each pair is plotted. If
Nonethe first available channel type from order shown above is used. Defaults to
New in version 0.21.
Whether to add markers for sensor locations. If
str, should be a valid matplotlib format string (e.g.,
'r+'for red plusses, see the Notes section of
True(the default), black circles will be used.
Labels for the sensors. If a
list, labels should correspond to the order of channels in
None(default), no channel names are plotted.
bool, shape (n_channels,) |
Array indicating channel(s) to highlight with a distinct plotting style. Array elements set to
Truewill be plotted with the parameters given in
mask_params. Defaults to
None, 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 number of contour lines to draw. If
0, no contours will be drawn. If a positive integer, that number of contour levels are chosen using the matplotlib tick locator (may sometimes be inaccurate, use array for accuracy). If array-like, the array values are used as the contour levels. The values should be in µV for EEG, fT for magnetometers and fT/m for gradiometers. If
colorbar=True, the colorbar will have ticks corresponding to the contour levels. Default is
- outlines‘head’ |
The outlines to be drawn. If ‘head’, the default head scheme will be drawn. 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’.
float| array_like | instance of
None| ‘auto’ | ‘eeglab’
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
ConductorModelto use the origin and radius from that object. If
'auto'the sphere is fit to digitization points. If
'eeglab'the head circle is defined by EEG electrodes
'Fpz'is not present, it will be approximated from the coordinates of
None(the default) is equivalent to
'auto'when enough extra digitization points are available, and (0, 0, 0, 0.095) otherwise.
New in version 0.20.
Changed in version 1.1: Added
The image interpolation to be used. Options are
'cubic'(default) to use
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.
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.
The resolution of the topomap image (number of pixels along each side).
Side length of each subplot in inches.
- cmapmatplotlib colormap |
Colormap to use. If None, ‘Reds’ is used for all positive data, otherwise defaults to ‘RdBu_r’.
tupleof length 2
Colormap limits to use. If a
tupleof floats, specifies the lower and upper bounds of the colormap (in that order); providing
Nonefor either entry will set the corresponding boundary at the min/max of the data. Defaults to
New in version 1.2.
How to normalize the colormap. If
None, standard linear normalization is performed. If not
vmaxwill be ignored. See Matplotlib docs for more details on colormap normalization, and the ERDs example for an example of its use.
New in version 0.24.
- axesinstance of
The axes to plot to. If
None, a new
Figurewill be created. Default is
Changed in version 1.2: If
axes=None, a new
Figureis created instead of plotting into the current axes.
Show the figure if
A function to be called when the user selects a set of channels by click-dragging (uses a matplotlib
Noneinteractive channel selection is disabled. Defaults to
The interpolated data.
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