mne.viz.plot_evoked_topomap#
- 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)[source]#
Plot topographic maps of specific time points of evoked data.
- Parameters:
- evoked
Evoked
The Evoked object.
- times
float
|array
offloat
| “auto” | “peaks” | “interactive” The time point(s) to plot. If “auto”, the number of
axes
determines the amount of time point(s). Ifaxes
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. If “interactive”, the time can be set interactively at run-time by using a slider.- average
float
| array_like offloat
, shape (n_times,) |None
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
array-like
object (e.g.,[0.1, 0.2, 0.3]
). IfNone
(default), no averaging will take place.Changed in version 1.1: Support for
array-like
input.- 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. IfNone
the first available channel type from order shown above is used. Defaults toNone
.- scalings
dict
|float
|None
The scalings of the channel types to be applied for plotting. If None, defaults to
dict(eeg=1e6, grad=1e13, mag=1e15)
.- proj
bool
| ‘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.
- sensors
bool
|str
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 ofplot()
). IfTrue
(the default), black circles will be used.- show_names
bool
|callable()
If
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 functionlambda x: x.replace('MEG ', '')
. Ifmask
is notNone
, only non-masked sensor names will be shown.- mask
ndarray
ofbool
, shape (n_channels, n_times) |None
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
True
will be plotted with the parameters given inmask_params
. Defaults toNone
, equivalent to an array of allFalse
elements.- 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)
- contours
int
| array_like 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. Ifcolorbar=True
, the colorbar will have ticks corresponding to the contour levels. Default is6
.- outlines‘head’ |
dict
|None
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’.
- sphere
float
| array_like | instance ofConductorModel
|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
ConductorModel
to 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'
,'Oz'
,'T7'
, and'T8'
(if'Fpz'
is not present, it will be approximated from the coordinates of'Oz'
).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
'eeglab'
option.- image_interp
str
The image interpolation to be used. Options are
'cubic'
(default) to usescipy.interpolate.CloughTocher2DInterpolator
,'nearest'
to usescipy.spatial.Voronoi
or'linear'
to usescipy.interpolate.LinearNDInterpolator
.- extrapolate
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.
- border
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.
- res
int
The resolution of the topomap image (number of pixels along each side).
- size
float
Side length of each subplot in inches.
- cmapmatplotlib 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. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. IfNone
,'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 toNone
.Warning
Interactive mode works smoothly only for a small amount of topomaps. Interactive mode is disabled by default for more than 2 topomaps.
- vlim
tuple
of length 2 | ‘joint’ Colormap limits to use. If a
tuple
of floats, specifies the lower and upper bounds of the colormap (in that order); providingNone
for either entry will set the corresponding boundary at the min/max of the data (separately for each topomap). Elements of thetuple
may also be callable functions which take in aNumPy array
and return a scalar. Ifvlim='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(None, None)
.New in version 1.2.
- cnorm
matplotlib.colors.Normalize
|None
How to normalize the colormap. If
None
, standard linear normalization is performed. If notNone
,vmin
andvmax
will be ignored. See Matplotlib docs for more details on colormap normalization, and the ERDs example for an example of its use.New in version 1.2.
- colorbar
bool
Plot a colorbar in the rightmost column of the figure.
- cbar_fmt
str
Formatting string for colorbar tick labels. See Format Specification Mini-Language for details.
- units
dict
|str
|None
The units to use for the colorbar label. Ignored if
colorbar=False
. IfNone
andscalings=None
the unit is automatically determined, otherwise the label will be “AU” indicating arbitrary units. Default isNone
.- axesinstance of
Axes
|list
ofAxes
|None
The axes to plot to. If
None
, a newFigure
will be created with the correct number of axes. IfAxes
are provided (either as a single instance or alist
of axes), the number of axes provided must match the number oftimes
provided (unlesstimes
isNone
).Default isNone
.- time_unit
str
The units for the time axis, can be “ms” or “s” (default).
New in version 0.16.
- time_format
str
|None
String format for topomap values. Defaults (None) to “%01d ms” if
time_unit='ms'
, “%0.3f s” iftime_unit='s'
, and “%g” otherwise. Can be an empty string to omit the time label.- nrows, ncols
int
| ‘auto’ The number of rows and columns of topographies to plot. If either
nrows
orncols
is'auto'
, the necessary number will be inferred. Defaults tonrows=1, ncols='auto'
. Ignored when times == ‘interactive’.New in version 0.20.
- show
bool
Show the figure if
True
.
- evoked
- Returns:
- figinstance of
matplotlib.figure.Figure
The figure.
- figinstance of
Notes
When existing
axes
are provided andcolorbar=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 whenaxes
are provided; use Matplotlib’saxes.set_position()
method or gridspec interface to adjust the colorbar size yourself.