mne.viz.plot_tfr_topomap#
- mne.viz.plot_tfr_topomap(tfr, tmin=None, tmax=None, fmin=0.0, fmax=inf, *, ch_type=None, baseline=None, mode='mean', 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=2, cmap=None, vlim=(None, None), vmin=None, vmax=None, cnorm=None, colorbar=True, cbar_fmt='%1.1e', unit=None, units=None, axes=None, title=None, show=True)[source]#
Plot topographic maps of specific time-frequency intervals of TFR data.
- Parameters:
- tfr
AverageTFR
The AverageTFR object.
- tmin, tmax
float
|None
First and last times to include, in seconds.
None
uses the first or last time present in the data. Default istmin=None, tmax=None
(all times).- fmin, fmax
float
The lower- and upper-bound on frequencies of interest. Default is
fmin=0, fmax=np.inf
(spans all frequencies present in the data).- ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ |
None
The channel type to plot. For
'grad'
, the gradiometers are collected in pairs and the mean for each pair is plotted. IfNone
the first available channel type from order shown above is used. Defaults toNone
.- baseline
tuple
orlist
of length 2 The time interval to apply rescaling / baseline correction. If None do not apply it. If baseline is (a, b) the interval is between “a (s)” and “b (s)”. If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) the whole time interval is used.
- mode‘mean’ | ‘ratio’ | ‘logratio’ | ‘percent’ | ‘zscore’ | ‘zlogratio’ |
None
Perform baseline correction by
subtracting the mean baseline power (‘mean’)
dividing by the mean baseline power (‘ratio’)
dividing by the mean baseline power and taking the log (‘logratio’)
subtracting the mean baseline power followed by dividing by the mean baseline power (‘percent’)
subtracting the mean baseline power and dividing by the standard deviation of the baseline power (‘zscore’)
dividing by the mean baseline power, taking the log, and dividing by the standard deviation of the baseline power (‘zlogratio’)
If None no baseline correction is applied.
- 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’ | ‘skirt’ |
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’.
Deprecated since version v1.2: The
outlines='skirt'
option is no longer supported and will raise an error starting in version 1.3. Passoutlines='head', sphere='eeglab'
for similar behavior.- 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.
- 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 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. Defaults to(None, None)
.New in version 1.2.
- vmin, vmax
float
|callable()
|None
Lower and upper bounds of the colormap, in the same units as the data. If
vmin
andvmax
are bothNone
, they are set at ± the maximum absolute value of the data (yielding a colormap with midpoint at 0). If only one ofvmin
,vmax
isNone
, will usemin(data)
ormax(data)
, respectively. If callable, should accept aNumPy array
of data and return a float.Deprecated since version v1.2: The
vmin
andvmax
parameters will be removed in version 1.3. Please use thevlim
parameter instead.- 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.
- unit
str
|None
The unit of the channel type used for colorbar labels.
Deprecated since version v1.2: The “unit” parameter is deprecated and will be removed in v1.3. Use “units” instead.
- units
str
|None
The units of the channel type; used for the colorbar label. Ignored if
colorbar=False
. IfNone
the label will be “AU” indicating arbitrary units. Default isNone
.- axesinstance of
Axes
|None
The axes to plot to. If
None
, a newFigure
will be created. Default isNone
.- title
str
|None
The title of the generated figure. If
None
(default), no title is displayed.Deprecated since version v1.2: The
title
parameter will be removed in version 1.3. Please usefig.suptitle()
instead.- show
bool
Show the figure if
True
.
- tfr
- Returns:
- fig
matplotlib.figure.Figure
The figure containing the topography.
- fig