Plot evoked data as images.
Evoked
The evoked data.
str
| list
| slice
| None
Channels to include. Slices and lists of integers will be interpreted as
channel indices. In lists, channel type strings (e.g., ['meg',
'eeg']
) will pick channels of those types, channel name strings (e.g.,
['MEG0111', 'MEG2623']
will pick the given channels. Can also be the
string values “all” to pick all channels, or “data” to pick data
channels. None (default) will pick all channels. Note that channels in
info['bads']
will be included if their names or indices are
explicitly provided.
This parameter can also be used to set the order the channels
are shown in, as the channel image is sorted by the order of picks.
list
of str
| ‘bads’Channels names to exclude from being shown. If ‘bads’, the bad channels are excluded.
Scale plot with channel (SI) unit.
Show figure if True.
dict
| None
Color limits for plots (after scaling has been applied). e.g.
clim = dict(eeg=[-20, 20])
.
Valid keys are eeg, mag, grad, misc. If None, the clim parameter
for each channel equals the pyplot default.
tuple
| None
X limits for plots.
If true SSP projections are applied before display. If ‘interactive’, a check box for reversible selection of SSP projection vectors will be shown.
dict
| None
The units of the channel types used for axes labels. If None,
defaults to dict(eeg='µV', grad='fT/cm', mag='fT')
.
dict
| None
The scalings of the channel types to be applied for plotting. If None,`
defaults to dict(eeg=1e6, grad=1e13, mag=1e15)
.
dict
| None
The titles associated with the channels. If None, defaults to
dict(eeg='EEG', grad='Gradiometers', mag='Magnetometers')
.
Axes
| list
| dict
| None
The axes to plot to. If list, the list must be a list of Axes of
the same length as the number of channel types. If instance of
Axes, there must be only one channel type plotted.
If group_by
is a dict, this cannot be a list, but it can be a dict
of lists of axes, with the keys matching those of group_by
. In that
case, the provided axes will be used for the corresponding groups.
Defaults to None
.
Colormap. 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 scale. Up and down arrows can be used to change the
colormap. If ‘interactive’, translates to ('RdBu_r', True)
.
Defaults to 'RdBu_r'
.
If True, plot a colorbar. Defaults to True.
New in version 0.16.
ndarray
| None
An array of booleans of the same shape as the data. Entries of the
data that correspond to False
in the mask are masked (see
do_mask
below). Useful for, e.g., masking for statistical
significance.
New in version 0.16.
None
| ‘both’ | ‘contour’ | ‘mask’If mask
is not None: if ‘contour’, a contour line is drawn around
the masked areas (True
in mask
). If ‘mask’, entries not
True
in mask
are shown transparently. If ‘both’, both a contour
and transparency are used.
If None
, defaults to ‘both’ if mask
is not None, and is ignored
otherwise.
New in version 0.16.
The colormap chosen for masked parts of the image (see below), if
mask
is not None
. If None, cmap
is reused. Defaults to
Greys
. Not interactive. Otherwise, as cmap
.
float
A float between 0 and 1. If mask
is not None, this sets the
alpha level (degree of transparency) for the masked-out segments.
I.e., if 0, masked-out segments are not visible at all.
Defaults to .25.
New in version 0.16.
str
The units for the time axis, can be “ms” or “s” (default).
New in version 0.16.
Determines if channel names should be plotted on the y axis. If False,
no names are shown. If True, ticks are set automatically by matplotlib
and the corresponding channel names are shown. If “all”, all channel
names are shown. If “auto”, is set to False if picks
is None
,
to True
if picks
contains 25 or more entries, or to “all”
if picks
contains fewer than 25 entries.
None
| dict
If a dict, the values must be picks, and axes
must also be a dict
with matching keys, or None. If axes
is None, one figure and one
axis will be created for each entry in group_by
.Then, for each
entry, the picked channels will be plotted to the corresponding axis.
If titles
are None, keys will become plot titles. This is useful
for e.g. ROIs. Each entry must contain only one channel type.
For example:
group_by=dict(Left_ROI=[1, 2, 3, 4], Right_ROI=[5, 6, 7, 8])
If None, all picked channels are plotted to the same axis.
float
| array-like | instance of ConductorModel
| 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. Currently the head radius
does not affect plotting.
New in version 0.20.
Changed in version 1.1: Added 'eeglab'
option.
matplotlib.figure.Figure
Figure containing the images.
mne.viz.plot_evoked_image
#Analysing continuous features with binning and regression in sensor space