- mne.viz.plot_evoked_topomap(evoked, times='auto', *, average=None, ch_type=None, scalings=None, proj=False, sensors=True, show_names=False, 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, colorbar=True, cbar_fmt='%3.1f', units=None, axes=None, time_unit='s', time_format=None, nrows=1, ncols='auto', show=True)#
Plot topographic maps of specific time points of evoked data.
The Evoked object.
float| “auto” | “peaks” | “interactive”
The time point(s) to plot. If “auto”, the number of
axesdetermines the amount of time point(s). If
axesis 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. If “interactive”, the time can be set interactively at run-time by using a slider.
float| array_like of
float, shape (n_times,) |
The time window (in seconds) around a given time point to be used for averaging. For example, 0.2 would translate into a time window that starts 0.1 s before and ends 0.1 s after the given time point. If the time window exceeds the duration of the data, it will be clipped. Different time windows (one per time point) can be provided by passing an
[0.1, 0.2, 0.3]). If
None(default), no averaging will take place.
Changed in version 1.1: Support for
- 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
The scalings of the channel types to be applied for plotting. If None, defaults to
dict(eeg=1e6, grad=1e13, mag=1e15).
- projbool | ‘interactive’ | ‘reconstruct’
If true SSP projections are applied before display. If ‘interactive’, a check box for reversible selection of SSP projection vectors will be shown. If ‘reconstruct’, projection vectors will be applied and then M/EEG data will be reconstructed via field mapping to reduce the signal bias caused by projection.
Changed in version 0.21: Support for ‘reconstruct’ was added.
- sensorsbool |
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.
- show_namesbool |
True, show channel names next to each sensor marker. If callable, 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
None, only non-masked sensor names will be shown.
ndarrayof bool, shape (n_channels, n_times) |
Array indicating channel-time combinations to highlight with a distinct plotting style (useful for, e.g. marking which channels at which times a statistical test of the data reaches significance). 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 v0.20.
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.
New in v0.18.
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.
Value to extrapolate to on the topomap borders. If
'mean'(default), then each extrapolated point has the average value of its neighbours.
New in v0.20.
The resolution of the topomap image (number of pixels along each side).
Side length of each subplot in inches.
- cmapmatplotlib colormap | (colormap, bool) | ‘interactive’ |
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. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If
'Reds'is used for data that is either all-positive or all-negative, and
'RdBu_r'is used otherwise.
'interactive'is equivalent to
(None, True). Defaults to
Interactive mode works smoothly only for a small amount of topomaps. Interactive mode is disabled by default for more than 2 topomaps.
tupleof length 2 | ‘joint’
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 (separately for each topomap). Elements of the
tuplemay also be callable functions which take in a
NumPy arrayand return a scalar. If
vlim='joint', will compute the colormap limits jointly across all topomaps of the same channel type, using the min/max of the data for that channel type. Defaults to
New in v1.2.
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 v1.2.
Plot a colorbar in the rightmost column of the figure.
Formatting string for colorbar tick labels. See Format Specification Mini-Language for details.
The units to use for the colorbar label. Ignored if
scalings=Nonethe unit is automatically determined, otherwise the label will be “AU” indicating arbitrary units. Default is
- axesinstance of
The axes to plot to. If
None, a new
Figurewill be created with the correct number of axes. If
Axesare provided (either as a single instance or a
listof axes), the number of axes provided must match the number of
The units for the time axis, can be “ms” or “s” (default).
New in v0.16.
String format for topomap values. Defaults (None) to “%01d ms” if
time_unit='ms', “%0.3f s” if
time_unit='s', and “%g” otherwise. Can be an empty string to omit the time label.
- nrows, ncols
The number of rows and columns of topographies to plot. If either
'auto', the necessary number will be inferred. Defaults to
nrows=1, ncols='auto'. Ignored when times == ‘interactive’.
New in v0.20.
Show the figure if
- figinstance of
- figinstance of
axesare provided and
colorbar=True, note that the colorbar scale will only accurately reflect topomaps that are generated in the same call as the colorbar. Note also that the colorbar will not be resized automatically when
axesare provided; use Matplotlib’s
axes.set_position()method or gridspec interface to adjust the colorbar size yourself.
time=="interactive", the figure will publish and subscribe to the following UI events:
TimeChangewhenever a new time is selected.