mne.viz.plot_epochs_psd_topomap#
- mne.viz.plot_epochs_psd_topomap(epochs, bands=None, tmin=None, tmax=None, proj=False, *, bandwidth=None, adaptive=False, low_bias=True, normalization='length', ch_type=None, normalize=False, agg_fun=None, dB=False, sensors=True, names=None, mask=None, mask_params=None, contours=0, 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='auto', units=None, axes=None, show=True, n_jobs=None, verbose=None)[source]#
Warning
LEGACY: New code should use Epochs.compute_psd().plot_topomap().
Plot the topomap of the power spectral density across epochs.
- Parameters:
- epochsinstance of
Epochs
The epochs object.
- bands
None
|dict
|list
oftuple
The frequencies or frequency ranges to plot. If a
dict
, keys will be used as subplot titles and values should be either a single frequency (e.g.,{'presentation rate': 6.5}
) or a length-two sequence of lower and upper frequency band edges (e.g.,{'theta': (4, 8)}
). If a single frequency is provided, the plot will show the frequency bin that is closest to the requested value. IfNone
(the default), expands to:bands = {'Delta (0-4 Hz)': (0, 4), 'Theta (4-8 Hz)': (4, 8), 'Alpha (8-12 Hz)': (8, 12), 'Beta (12-30 Hz)': (12, 30), 'Gamma (30-45 Hz)': (30, 45)}
Note
For backwards compatibility,
tuples
of length 2 or 3 are also accepted, where the last element of the tuple is the subplot title and the other entries are frequency values (a single value or band edges). New code should usedict
orNone
.Changed in version 1.2: Allow passing a dict and discourage passing tuples.
- 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).- proj
bool
Whether to apply SSP projection vectors before spectral estimation. Default is
False
.- bandwidth
float
The bandwidth of the multi taper windowing function in Hz. The default value is a window half-bandwidth of 4 Hz.
- adaptive
bool
Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation).
- low_bias
bool
Only use tapers with more than 90% spectral concentration within bandwidth.
- normalization‘full’ | ‘length’
Normalization strategy. If “full”, the PSD will be normalized by the sampling rate as well as the length of the signal (as in Nitime). Default is
'length'
.- 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
.- normalize
bool
If True, each band will be divided by the total power. Defaults to False.
- agg_fun
callable()
The function used to aggregate over frequencies. Defaults to
numpy.sum()
ifnormalize=True
, elsenumpy.mean()
.- dB
bool
Whether to plot on a decibel-like scale. If
True
, plots 10 × log₁₀(spectral power) following the application ofagg_fun
. Ignored ifnormalize=True
.- 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.- names
None
|list
Labels for the sensors. If a
list
, labels should correspond to the order of channels indata
. IfNone
(default), no channel names are plotted.- 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.
- 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 0.21.
- 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. If
'auto'
, is equivalent to ‘%0.3f’ ifdB=False
and ‘%0.1f’ ifdB=True
. Defaults to'auto'
.- units
str
|None
The units to use for the colorbar label. Ignored if
colorbar=False
. IfNone
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 length ofbands
.Default isNone
.- show
bool
Show the figure if
True
.- n_jobs
int
|None
The number of jobs to run in parallel. If
-1
, it is set to the number of CPU cores. Requires thejoblib
package.None
(default) is a marker for ‘unset’ that will be interpreted asn_jobs=1
(sequential execution) unless the call is performed under ajoblib.parallel_backend()
context manager that sets another value forn_jobs
.- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- epochsinstance of
- Returns:
- figinstance of
Figure
Figure showing one scalp topography per frequency band.
- figinstance of